Component Interfaces¶
This sections contains the base classes for the different components within the Stone Soup Framework.
Enabling Components¶
-
class
stonesoup.detector.
Detector
(sensor)[source] Detector base class
A Detector processes
SensorData
to generateDetection
data.- Parameters
sensor (
SensorDataReader
) – Source of sensor data
-
sensor
: stonesoup.reader.base.SensorDataReader Source of sensor data
-
abstract
detections_gen
() Returns a generator of detections for each time step.
- Yields
datetime.datetime
– Datetime of current time stepset of
Detection
– Detections generate in the time step
-
class
stonesoup.feeder.
Feeder
(reader)[source] Feeder base class
Feeder consumes and outputs
State
data and can be used to modify the sequence, duplicate or drop data.- Parameters
reader (
Reader
) – Source of detections
-
reader
: stonesoup.reader.base.Reader Source of detections
-
class
stonesoup.metricgenerator.
MetricGenerator
[source] Metric Generator base class
Generates
Metric
objects used to asses the performance of a tracker using data held in aMetricManager
object-
abstract
compute_metric
(manager, **kwargs)[source] Compute metric
- Parameters
manager (MetricManager) – containing the data to be used to create the metric(s)
- Returns
Generated metrics
- Return type
list of
Metric
objects
-
abstract
-
class
stonesoup.smoother.
Smoother
(transition_model=None)[source] Smoother Base Class.
- Parameters
transition_model (
TransitionModel
, optional) – Transition Model.
-
transition_model
: stonesoup.models.transition.base.TransitionModel Transition Model.
-
class
stonesoup.tracker.
Tracker
[source] Tracker base class
-
abstract
tracks_gen
()[source] Returns a generator of tracks for each time step.
- Yields
datetime.datetime
– Datetime of current time stepset of
Track
– Tracks existing in the time step
-
abstract
Data Input¶
-
class
stonesoup.reader.
DetectionReader
[source] Detection Reader base class
-
abstract
detections_gen
()[source] Returns a generator of detections for each time step.
- Yields
datetime.datetime
– Datetime of current time stepset of
Detection
– Detections generate in the time step
-
abstract
-
class
stonesoup.reader.
GroundTruthReader
[source] Ground Truth Reader base class
-
abstract
groundtruth_paths_gen
()[source] Returns a generator of ground truth paths for each time step.
- Yields
datetime.datetime
– Datetime of current time stepset of
GroundTruthPath
– Ground truth paths existing in the time step
-
abstract
-
class
stonesoup.reader.
SensorDataReader
[source] Sensor Data Reader base class
-
abstract
sensor_data_gen
()[source] Returns a generator of sensor data for each time step.
- Yields
datetime.datetime
– Datetime of current time stepset of
SensorData
– Sensor data generated in the time step
-
abstract
Data Output¶
-
class
stonesoup.writer.
MetricsWriter
(metric_generator)[source] Metrics Writer base class.
Writes out metrics to some form of storage for analysis.
- Parameters
metric_generator (
MetricGenerator
) – Source of metric to be written out
-
metric_generator
: stonesoup.metricgenerator.base.MetricGenerator Source of metric to be written out
-
class
stonesoup.writer.
TrackWriter
(tracker)[source] Track Writer base class.
Writes out tracks to some form of storage for analysis.
- Parameters
tracker (
Tracker
) – Source of tracks to be written out
-
tracker
: stonesoup.tracker.base.Tracker Source of tracks to be written out
Simulation¶
-
class
stonesoup.simulator.
DetectionSimulator
[source] Detection Simulator base class
-
abstract
detections_gen
() Returns a generator of detections for each time step.
- Yields
datetime.datetime
– Datetime of current time stepset of
Detection
– Detections generate in the time step
-
abstract
-
class
stonesoup.simulator.
GroundTruthSimulator
[source] Ground truth simulator
-
abstract
groundtruth_paths_gen
() Returns a generator of ground truth paths for each time step.
- Yields
datetime.datetime
– Datetime of current time stepset of
GroundTruthPath
– Ground truth paths existing in the time step
-
abstract
-
class
stonesoup.simulator.
SensorSimulator
[source] Sensor Simulator base class
-
abstract
sensor_data_gen
() Returns a generator of sensor data for each time step.
- Yields
datetime.datetime
– Datetime of current time stepset of
SensorData
– Sensor data generated in the time step
-
abstract
-
class
stonesoup.platform.
Platform
(states, position_mapping, rotation_offsets=None, mounting_offsets=None, sensors=None, velocity_mapping=None)[source] A platform that can carry a number of different sensors.
The location of platform mounted sensors will be maintained relative to the sensor position. Platforms move within a 2 or 3 dimensional rectangular cartesian space.
A simple platform is considered to always be aligned with its principle velocity. It does not take into account issues such as bank angle or body deformation (e.g. flex).
Note
This class abstract and not intended to be instantiated. To get the behaviour of this class use a subclass which gives movement behaviours. Currently these are
FixedPlatform
andMovingPlatform
- Parameters
states (
Sequence[State]
) – A list of States which enables the platform’s history to be accessed in simulators and for plotting. Initiated as a state, for a static platform, this would usually contain its position coordinates in the form[x, y, z]
. For a moving platform it would contain position and velocity interleaved:[x, vx, y, vy, z, vz]
position_mapping (
Sequence[int]
) – Mapping between platform position and state vector. For a position-only 3d platform this might be[0, 1, 2]
. For a position and velocity platform:[0, 2, 4]
rotation_offsets (
MutableSequence[StateVector]
, optional) – A list of StateVectors containing the sensor rotation offsets from the platform’s primary axis (defined as the direction of motion). Defaults to a zero vector with the same length as the Platform’sposition_mapping
mounting_offsets (
MutableSequence[StateVector]
, optional) – A list of StateVectors containing the sensor translation offsets from the platform’s reference point. Defaults to a zero vector with the same length as the Platform’sposition_mapping
sensors (
MutableSequence[ForwardRef('BaseSensor')]
, optional) – A list of N mounted sensors. Defaults to an empty listvelocity_mapping (
Sequence[int]
, optional) – Mapping between platform velocity and state dims. If not set, it will default to[m+1 for m in position_mapping]
-
states
: Sequence[stonesoup.types.state.State] A list of States which enables the platform’s history to be accessed in simulators and for plotting. Initiated as a state, for a static platform, this would usually contain its position coordinates in the form
[x, y, z]
. For a moving platform it would contain position and velocity interleaved:[x, vx, y, vy, z, vz]
-
position_mapping
: Sequence[int] Mapping between platform position and state vector. For a position-only 3d platform this might be
[0, 1, 2]
. For a position and velocity platform:[0, 2, 4]
-
velocity_mapping
: Sequence[int] Mapping between platform velocity and state dims. If not set, it will default to
[m+1 for m in position_mapping]
-
property
position
Return the position of the platform.
Extracted from the state vector of the platform using the platform’s
position_mapping
. This property is settable for fixed platforms, but not for movable ones, where the position must be set by moving the model with a transition model.
-
sensors
: MutableSequence[BaseSensor] A list of N mounted sensors. Defaults to an empty list
-
mounting_offsets
: MutableSequence[stonesoup.types.array.StateVector] A list of StateVectors containing the sensor translation offsets from the platform’s reference point. Defaults to a zero vector with the same length as the Platform’s
position_mapping
-
rotation_offsets
: MutableSequence[stonesoup.types.array.StateVector] A list of StateVectors containing the sensor rotation offsets from the platform’s primary axis (defined as the direction of motion). Defaults to a zero vector with the same length as the Platform’s
position_mapping
-
property
ndim
Convenience property to return the number of dimensions in which the platform operates.
Given by the length of the
position_mapping
-
abstract property
orientation
Return the orientation of the platform.
Implementation is case dependent and left to the Fixed/Moving subclasses
-
abstract property
velocity
Return the velocity of the platform.
Implementation is case dependent and left to the Fixed/Moving subclasses
-
abstract
move
(timestamp: datetime.datetime, **kwargs) → None[source] Update the platform position using the
transition_model
.- Parameters
timestamp (
datetime.datetime
, optional) – A timestamp signifying when the end of the maneuver (the default isNone
)
Notes
This methods updates the value of
position
.Any provided
kwargs
are forwarded to thetransition_model
.If
transition_model
ortimestamp
isNone
, the method has no effect, but will return successfully.
-
add_sensor
(sensor: BaseSensor, mounting_offset: Optional[stonesoup.types.array.StateVector] = None, rotation_offset: Optional[stonesoup.types.array.StateVector] = None) → None[source] Add a sensor to the platform
- Parameters
sensor (
BaseSensor
) – The sensor object to addmounting_offset (
StateVector
, optional) – A StateVector with the mounting offset of the new sensor. If not supplied, defaults to a zero vectorrotation_offset (
StateVector
, optional) – A StateVector with the rotation offset of the new sensor. If not supplied, defaults to a zero vector.
-
remove_sensor
(sensor: BaseSensor) → None[source] Remove a sensor from the platform
- Parameters
sensor (
BaseSensor
) – The sensor object to remove
-
pop_sensor
(index: int)[source] Remove a sensor from the platform by index
- Parameters
index (int) – The index of the sensor to remove
-
get_sensor_position
(sensor: BaseSensor) → stonesoup.types.array.StateVector[source] Return the position of the given sensor, which should be already attached to the platform. If the sensor is not attached to the platform, raises a
ValueError
.- Parameters
sensor (
BaseSensor
) – The sensor for which to return the position.- Returns
The position of the sensor, taking into account the platform position and orientation and the mounting offset of the sensor.
- Return type
StateVector
-
get_sensor_orientation
(sensor: BaseSensor) → stonesoup.types.array.StateVector[source] Return the orientation of the given sensor, which should be already attached to the platform. If the sensor is not attached to the platform, raises a
ValueError
.- Parameters
sensor (
BaseSensor
) – The sensor for which to return the orientation.- Returns
The orientation of the sensor, taking into account the platform orientation and the rotation offset of the sensor.
- Return type
StateVector
-
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.
-
insert
(index, value) S.insert(index, value) – insert value before index
-
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.sensor.sensor.
Sensor
[source] Sensor Base class for general use.
Most properties and methods are inherited from
BaseSensor
, but this class includes crucial functionality and so should be used in preference.All sensors must be mounted on a platform to calculate their position and orientation. To make this easier, if the sensor has a position and/or orientation specified in the constructor, and no
platform_system
, then the default is to create an internally held “private” platform for the Sensor. This restricts the later setting of theplatform_system
but does allow the Sensor to control (and set) its own position and orientation.-
property
orientation
A 3x1 StateVector of angles (rad), specifying the sensor orientation in terms of the counter-clockwise rotation around each Cartesian axis in the order \(x,y,z\). The rotation angles are positive if the rotation is in the counter-clockwise direction when viewed by an observer looking along the respective rotation axis, towards the origin.
Note
This property delegates the actual calculation of orientation to the platform on which the sensor is mounted.
It is settable if, and only if, the sensor holds its own internal platform.
-
property
platform
Return the platform system to which the sensor is attached. Resolves the
weakref
stored in theplatform_system
Property.
-
property
platform_system
Return a
weakref
to the platform on which the sensor is mounted
-
property
position
The sensor position on a 3D Cartesian plane, expressed as a 3x1
StateVector
of Cartesian coordinates in the order \(x,y,z\).Note
This property delegates the actual calculation of position to the platform on which the sensor is mounted.
It is settable if, and only if, the sensor holds its own internal platform.
-
property
velocity
The sensor velocity on a 3D Cartesian plane, expressed as a 3x1
StateVector
of Cartesian coordinates in the order \(x,y,z\).Note
This property delegates the actual calculation of velocity to the platform on which the sensor is mounted.
It is settable if, and only if, the sensor holds its own internal platform which is a MovingPlatfom.
-
property
Algorithm Components¶
-
class
stonesoup.dataassociator.
DataAssociator
(hypothesiser)[source] Data Associator base class
A data associator is used to associate tracks and detections, and may also include an association of a missed detection. The associations generate are in the form a mapping each track to a hypothesis, based on “best” choice from hypotheses generate from a
Hypothesiser
.- Parameters
hypothesiser (
Hypothesiser
) – Generate a set of hypotheses for each track-detection pair
-
hypothesiser
: stonesoup.hypothesiser.base.Hypothesiser Generate a set of hypotheses for each track-detection pair
-
abstract
associate
(tracks, detections, timestamp=None, **kwargs)[source] Associate tracks and detections
- Parameters
tracks (set of
Track
) – Tracks which detections will be associated to.detections (set of
Detection
) – Detections to be associated to tracks.timestamp (
datetime.datetime
) – Timestamp to be used for missed detections.
- Returns
Mapping of track to Hypothesis
- Return type
mapping of
Track
:Hypothesis
-
static
isvalid
(joint_hypothesis)[source] Determine whether a joint_hypothesis is valid.
Check the set of hypotheses that define a joint hypothesis to ensure a single detection is not associated to more than one track.
- Parameters
joint_hypothesis (
JointHypothesis
) – A set of hypotheses linking each prediction to a single detection- Returns
Whether joint_hypothesis is a valid set of hypotheses
- Return type
-
classmethod
enumerate_joint_hypotheses
(hypotheses)[source] Enumerate the possible joint hypotheses.
Create a list of all possible joint hypotheses from the individual hypotheses and determine whether each is valid.
- Parameters
hypotheses (list of
Hypothesis
) – A list of all hypotheses linking predictions to detections, including missed detections- Returns
joint_hypotheses – A list of all valid joint hypotheses with a score on each
- Return type
list of
JointHypothesis
-
class
stonesoup.deleter.
Deleter
[source] Deleter base class.
Proposes tracks for deletion.
-
abstract
check_for_deletion
(track, **kwargs)[source] Abstract method to check if a given track should be deleted
-
delete_tracks
(tracks, **kwargs)[source] Generic/Base track deletion method.
Iterates through all tracks in a given list and calls
check_for_deletion()
to determine which tracks should be deleted and which should survive.
-
abstract
-
class
stonesoup.hypothesiser.
Hypothesiser
[source] Hypothesiser base class
Given a track and set of detections, generate hypothesis of association.
-
hypothesise
(track, detections, **kwargs)[source] Hypothesise track and detection association
- Parameters
track (Track) – Track which hypotheses will be generated for.
detections – Detections used to generate hypotheses.
- Returns
Ordered sequence of “best” to “worse” hypothesis.
- Return type
sequence of
Hypothesis
-
-
class
stonesoup.gater.
Gater
(hypothesiser)[source] Gater base class
Gaters wrap
Hypothesiser
objects and can be used to modify (typically reduce) the returned hypotheses.- Parameters
hypothesiser (
Union[Hypothesiser, ForwardRef('Gater')]
) – Hypothesiser or Gater that is being wrapped.
-
hypothesiser
: Union[stonesoup.hypothesiser.base.Hypothesiser, stonesoup.gater.base.Gater] Hypothesiser or Gater that is being wrapped.
-
class
stonesoup.initiator.
Initiator
[source] Initiator base class
Creates zero or more tracks based on provided detections.
-
class
stonesoup.mixturereducer.
MixtureReducer
[source] Mixture Reducer base class
-
class
stonesoup.predictor.
Predictor
(transition_model, control_model=None)[source] Predictor base class
A predictor is used to predict a new
State
given a priorState
and aTransitionModel
. In addition, aControlModel
may be used to model an external influence on the state.\[\mathbf{x}_{k|k-1} = f_k(\mathbf{x}_{k-1}, \mathbf{\nu}_k) + b_k(\mathbf{u}_k, \mathbf{\eta}_k)\]where \(\mathbf{x}_{k-1}\) is the prior state, \(f_k(\mathbf{x}_{k-1})\) is the transition function, \(\mathbf{u}_k\) the control vector, \(b_k(\mathbf{u}_k)\) the control input and \(\mathbf{\nu}_k\) and \(\mathbf{\eta}_k\) the transition and control model noise respectively.
- Parameters
transition_model (
TransitionModel
) – transition modelcontrol_model (
ControlModel
, optional) – control model
-
transition_model
: stonesoup.models.transition.base.TransitionModel transition model
-
control_model
: stonesoup.models.control.base.ControlModel control model
-
abstract
predict
(prior, timestamp=None, **kwargs)[source] The prediction function itself
- Parameters
prior (
State
) – The prior statetimestamp (
datetime.datetime
, optional) – Time at which the prediction is made (used by the transition model)
- Returns
State prediction
- Return type
-
class
stonesoup.resampler.
Resampler
[source] Resampler base class
-
class
stonesoup.updater.
Updater
(measurement_model)[source] Updater base class
An updater is used to update the predicted state, utilising a measurement and a
MeasurementModel
. The general observation model is\[\mathbf{z} = h(\mathbf{x}, \mathbf{\sigma})\]where \(\mathbf{x}\) is the state, \(\mathbf{\sigma}\), the measurement noise and \(\mathbf{z}\) the resulting measurement.
- Parameters
measurement_model (
MeasurementModel
) – measurement model
-
measurement_model
: stonesoup.models.measurement.base.MeasurementModel measurement model
-
abstract
predict_measurement
(state_prediction, measurement_model=None, **kwargs)[source] Get measurement prediction from state prediction
- Parameters
state_prediction (
StatePrediction
) – The state predictionmeasurement_model (
MeasurementModel
, optional) – The measurement model used to generate the measurement prediction. Should be used in cases where the measurement model is dependent on the received measurement. The default is None, in which case the updater will use the measurement model specified on initialisation
- Returns
The predicted measurement
- Return type
-
abstract
update
(hypothesis, **kwargs)[source] Update state using prediction and measurement.
- Parameters
hypothesis (
Hypothesis
) – Hypothesis with predicted state and associated detection used for updating.- Returns
The state posterior
- Return type
Models¶
-
class
stonesoup.models.control.
ControlModel
(ndim_state, mapping)[source] Control Model base class
- Parameters
ndim_state (
int
) – Number of state dimensionsmapping (
Sequence[int]
) – Mapping between control and state dims
-
ndim_state
: int Number of state dimensions
-
mapping
: Sequence[int] Mapping between control and state dims
-
abstract property
ndim_ctrl
Number of control input dimensions
-
abstract
function
(state, noise=False) Model function
-
abstract property
ndim
Number of dimensions of model
-
abstract
pdf
(state1, state2) Model pdf/likelihood evaluator method
-
abstract
rvs
(num_samples=1) Model noise/sample generation method
-
class
stonesoup.models.measurement.
MeasurementModel
(ndim_state, mapping)[source] Measurement Model base class
- Parameters
ndim_state (
int
) – Number of state dimensionsmapping (
Sequence[int]
) – Mapping between measurement and state dims
-
ndim_state
: int Number of state dimensions
-
mapping
: Sequence[int] Mapping between measurement and state dims
-
property
ndim
Number of dimensions of model
-
abstract property
ndim_meas
Number of measurement dimensions
-
abstract
function
(state, noise=False) Model function
-
abstract
pdf
(state1, state2) Model pdf/likelihood evaluator method
-
abstract
rvs
(num_samples=1) Model noise/sample generation method
-
class
stonesoup.models.transition.
TransitionModel
[source] Transition Model base class
-
property
ndim
Number of dimensions of model
-
abstract property
ndim_state
Number of state dimensions
-
abstract
function
(state, noise=False) Model function
-
abstract
pdf
(state1, state2) Model pdf/likelihood evaluator method
-
abstract
rvs
(num_samples=1) Model noise/sample generation method
-
property