#
# ISC License
#
# Copyright (c) 2016, Autonomous Vehicle Systems Lab, University of Colorado at Boulder
#
# Permission to use, copy, modify, and/or distribute this software for any
# purpose with or without fee is hereby granted, provided that the above
# copyright notice and this permission notice appear in all copies.
#
# THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES
# WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF
# MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR
# ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES
# WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN
# ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF
# OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.
#
r"""
Overview
--------
This script sets up a 6-DOF spacecraft in deep space without any gravitational
bodies. Only rotational motion is simulated. The script illustrates how to
setup attitude filters that use measurements from the Coarse Sun Sensors (CSS).
The script is found in the folder ``basilisk/examples`` and executed by using::
python3 scenarioCSSFilters.py
When the simulation completes several plots are written summarizing the filter performances.
The simulation reads the Sun's position from :ref:`SpiceInterface`. By creating this
spice object and adding it to the task, the spice object automatically writes out
the ephemeris messages.
The dynamics simulation is setup using a :ref:`Spacecraft` module where a specific
spacecraft location is specified. Note that both the rotational and translational
degrees of freedom of the spacecraft hub are turned on here to get a 6-DOF simulation.
The position vector is required when computing the relative heading between the sun
and the spacecraft locations. The spacecraft position is held fixed, while the
orientation rotates constantly about the 3rd body axis.
The CSS modules must first be individual created and configured. This simulation
uses 8 sun sensors, in 2 pyramids of 4 units. The code that sets up a constellation
displays another method that is used in :ref:`scenarioCSS`. In this
case instead of creating a list of CSS and adding the list to the constellation,
the ``appendCSS`` command is used.
The constellation characteristics are summarized in the following table.
This table shows the individual unit vectors for each sensor, named ``nHat_B`` in the code.
======== ============================
CSS normal vector
======== ============================
1 [:math:`\sqrt{2}`/2, -0.5, 0.5]
2 [:math:`\sqrt{2}`/2, -0.5, -0.5]
3 [:math:`\sqrt{2}`/2, 0.5, -0.5]
4 [:math:`\sqrt{2}`/2, 0.5, 0.5]
5 [-:math:`\sqrt{2}`/2, 0, :math:`\sqrt{2}`/2]
6 [-:math:`\sqrt{2}`/2, :math:`\sqrt{2}`/2, 0]
7 [-:math:`\sqrt{2}`/2, 0, -:math:`\sqrt{2}`/2]
8 [-:math:`\sqrt{2}`/2, -:math:`\sqrt{2}`/2, 0]
======== ============================
An additional message must be written for the configuration of the CSS for the
Flight Software modules. This is done with ``vehicleConfigData``, a message that is
read once at the start of a simulation. This message also allows the user to set
different values between the simulation and the flight software parameters,
which could corrupt the simulation, and reproduce an imperfect spacecraft
construction process.
The filters can now be initialized. These are configured very similarly, but
the nature of the filters lead to slight differences. All of the filters output
a Navigation message which outputs the sun heading for other modules to use,
but they also output a filtering message (:ref:`sunlineFilterMsgPayload`), containing
observations, post-fit residuals, covariances, and full states. This allows users
to check in on filter performances efficiently, and is used in this tutorial.
This first allows us to see when observations occur throughout the scenario
over which we are comparing performance:
.. image:: /_images/Scenarios/scenario_Filters_ObsOEKF.svg
:align: center
Setup 1 - ukF
-------------
In the first run, we use an square root unscented Kalman Filter (:ref:`sunlineUKF`).
This filter has the following states:
================ =============
States notation
================ =============
Sunheading ``d``
Sunheading Rate| ``d_dot``
================ =============
This filter estimates sunheading, and the sunheading's rate of change.
As a unscented filter, it also has the the following parameters:
============= =============
Name Value
============= =============
``alpha`` 0.02
``beta`` 2
``kappa`` 0
============= =============
The covariance is then set, as well as the measurement noise:
============================================= ==================
Parameter Value
============================================= ==================
covariance on heading vector components 0.2
covariance on heading rate components 0.02
noise on heading measurements 0.017 ** 2
noise on heading measurements 0.0017 ** 2
============================================= ==================
The resulting plots of the states, their covariance envelopes, as compared
to the true state are plotted. Further documentation can be found in :ref:`sunlineUKF`.
.. image:: /_images/Scenarios/scenario_Filters_StatesPlotuKF.svg
:align: center
.. image:: /_images/Scenarios/scenario_Filters_StatesExpecteduKF.svg
:align: center
These plots show good state estimation throughout the simulation.
The mean stays close to the truth, the states do appear slightly noisy at times.
The post fit residuals, show a fully functional filter, with no issues of observability:
.. image:: /_images/Scenarios/scenario_Filters_PostFituKF.svg
:align: center
Setup 2 - EKF
-------------
The following filter tested is an Extended Kalman filter (:ref:`sunlineEKF`).
This filter uses all the same values for initialization as the uKF (aside
from the uKF specific alpha, beta, kappa variables). A couple variables are added:
=============== ===============
Name Value
=============== ===============
Process noise 0.001**2
CKF switch 5
=============== ===============
The process noise is the noise added on the dynamics. This allows to account
for dynamical uncertainties, and avoid filter saturation.
The CKF switch is the number of measurements that are processed using a classical,
linear Kalman filter when the filter is first run. This allows for the covariance
to shrink before employing the EKF, increasing the robustness.
The states vs expected states are plotted, as well as the state error plots along with the covariance
envelopes. Further documentation can be found in :ref:`sunlineEKF`
.. image:: /_images/Scenarios/scenario_Filters_StatesPlotEKF.svg
:align: center
.. image:: /_images/Scenarios/scenario_Filters_StatesExpectedEKF.svg
:align: center
These plots show good state estimation throughout the simulation and despite the patches of time with
fewer measurements. The covariance stays close to the mean, without exesive noise.
The post fit residuals, give further confirmation of a working filter:
.. image:: /_images/Scenarios/scenario_Filters_PostFitEKF.svg
:align: center
Setup 3 - OEKF
--------------
The 3rd scenario uses a second type of Extended Kalman Filter (:ref:`okeefeEKF`).
This filter takes in fewer states as it only estimates the sun heading. In order to
propagate it, it estimates the omega vector from the two last measurements.
The set up is nearly identical to the EKF, with the exception of the size of the
vectors and matrices (only 3 states are estimated now). Furthermore, the
rotation rate of the spacecraft, omega, is initialized. More in-depth documentation on the filter
specifics are found in :ref:`okeefeEKF`.
.. image:: /_images/Scenarios/scenario_Filters_StatesPlotOEKF.svg
:align: center
.. image:: /_images/Scenarios/scenario_Filters_StatesExpectedOEKF.svg
:align: center
These plots show poorer state estimation throughout the simulation. As measurements stop,
the filter doesn't propagate the states sufficiently well. This is due to the absence of
rate in the states, and the compensation with the computation of omega can lead to
noisy estimates.
The post fit residuals, do show that the filter is working, just with difficulties
when measurements become sparse:
.. image:: /_images/Scenarios/scenario_Filters_PostFitOEKF.svg
:align: center
Setup 4 -Switch-EKF
-------------------
The 4th scenario uses a Switch formulation to extract the observable rates as
well as estimate the sun heading (:ref:`sunlineSEKF`).
.. image:: /_images/Scenarios/scenario_Filters_StatesPlotSEKF.svg
:align: center
.. image:: /_images/Scenarios/scenario_Filters_StatesExpectedSEKF.svg
:align: center
These plots show poorer state estimation throughout the simulation.
The post fit residuals show that the filter is working, just with difficulties
when measurements become sparse.
.. image:: /_images/Scenarios/scenario_Filters_PostFitSEKF.svg
:align: center
Setup 5 -Switch-uKF
-------------------
The 5th scenario uses the same Switch formulation but in a square-root uKF (:ref:`sunlineSuKF`).
This one has an additional state: the sun intensity (equal to 1 at 1AU). This state
has low process noise and low initial covariance given it's well determined nature generally.
.. image:: /_images/Scenarios/scenario_Filters_StatesPlotSuKF.svg
:align: center
.. image:: /_images/Scenarios/scenario_Filters_StatesExpectedSuKF.svg
:align: center
These plots show good state estimation throughout the simulation.
The mean stays close to the truth.
The post fit residuals, show a fully functional filter, with no issues of observabilty:
.. image:: /_images/Scenarios/scenario_Filters_PostFitSuKF.svg
:align: center
"""
#
# Basilisk Scenario Script and Integrated Test
#
# Purpose: Demonstrates how to setup and use sun heading filters
# Author: Thibaud Teil
# Creation Date: November 20, 2017
#
import numpy as np
from Basilisk import __path__
bskPath = __path__[0]
from Basilisk.utilities import SimulationBaseClass, unitTestSupport, macros
import matplotlib.pyplot as plt
from Basilisk.utilities import orbitalMotion as om
from Basilisk.utilities import RigidBodyKinematics as rbk
from Basilisk.simulation import spacecraft, coarseSunSensor
from Basilisk.fswAlgorithms import sunlineUKF, sunlineEKF, okeefeEKF, sunlineSEKF, sunlineSuKF
from Basilisk.architecture import messaging
import SunLineKF_test_utilities as Fplot
[docs]def setupUKFData(filterObject):
"""Setup UKF Filter Data"""
filterObject.alpha = 0.02
filterObject.beta = 2.0
filterObject.kappa = 0.0
filterObject.state = [1.0, 0.1, 0.0, 0.0, 0.01, 0.0]
filterObject.covar = [1., 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 1., 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 1., 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.02, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.02, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.02]
qNoiseIn = np.identity(6)
qNoiseIn[0:3, 0:3] = qNoiseIn[0:3, 0:3]*0.017*0.017
qNoiseIn[3:6, 3:6] = qNoiseIn[3:6, 3:6]*0.0017*0.0017
filterObject.qNoise = qNoiseIn.reshape(36).tolist()
filterObject.qObsVal = 0.017**2
filterObject.sensorUseThresh = np.sqrt(filterObject.qObsVal)*5
[docs]def setupEKFData(filterObject):
"""Setup EKF Filter Data"""
filterObject.state = [1.0, 0.1, 0.0, 0.0, 0.01, 0.0]
filterObject.x = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
filterObject.covar = [1., 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 1., 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 1., 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.02, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.02, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.02]
filterObject.qProcVal = 0.001**2
filterObject.qObsVal = 0.017**2
filterObject.sensorUseThresh = np.sqrt(filterObject.qObsVal)*5
filterObject.eKFSwitch = 5. #If low (0-5), the CKF kicks in easily, if high (>10) it's mostly only EKF
[docs]def setupOEKFData(filterObject):
"""Setup OEKF Filter Data"""
filterObject.omega = [0., 0., 0.]
filterObject.state = [1.0, 0.1, 0.0]
filterObject.x = [0.0, 0.0, 0.0]
filterObject.covar = [1., 0.0, 0.0,
0.0, 1., 0.0,
0.0, 0.0, 1.]
filterObject.qProcVal = 0.1**2
filterObject.qObsVal = 0.017 ** 2
filterObject.sensorUseThresh = np.sqrt(filterObject.qObsVal)*5
filterObject.eKFSwitch = 5. #If low (0-5), the CKF kicks in easily, if high (>10) it's mostly only EKF
[docs]def setupSEKFData(filterObject):
"""Setup SEKF Filter Data"""
filterObject.state = [1.0, 0.1, 0., 0., 0.]
filterObject.x = [0.0, 0.0, 0.0, 0.0, 0.0]
filterObject.covar = [1., 0.0, 0.0, 0.0, 0.0,
0.0, 1., 0.0, 0.0, 0.0,
0.0, 0.0, 1., 0.0, 0.0,
0.0, 0.0, 0.0, 0.01, 0.0,
0.0, 0.0, 0.0, 0.0, 0.01]
filterObject.qProcVal = 0.001**2
filterObject.qObsVal = 0.017 ** 2
filterObject.sensorUseThresh = np.sqrt(filterObject.qObsVal)*5
filterObject.eKFSwitch = 5. #If low (0-5), the CKF kicks in easily, if high (>10) it's mostly only EKF
[docs]def setupSuKFData(filterObject):
"""Setup SuKF Filter Data"""
filterObject.alpha = 0.02
filterObject.beta = 2.0
filterObject.kappa = 0.0
filterObject.stateInit = [1.0, 0.1, 0., 0., 0., 1.]
filterObject.covarInit = [1., 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 1., 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 1., 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.01, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.01, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0001]
qNoiseIn = np.identity(6)
qNoiseIn[0:3, 0:3] = qNoiseIn[0:3, 0:3]*0.001**2
qNoiseIn[3:5, 3:5] = qNoiseIn[3:5, 3:5]*0.0001**2
qNoiseIn[5, 5] = qNoiseIn[5, 5]*0.000001**2
filterObject.qNoise = qNoiseIn.reshape(36).tolist()
filterObject.qObsVal = 0.017**2
filterObject.sensorUseThresh = np.sqrt(filterObject.qObsVal)*5
[docs]def run(saveFigures, show_plots, FilterType, simTime):
"""
At the end of the python script you can specify the following example parameters.
Args:
saveFigures (bool): flag to save off the figures
show_plots (bool): Determines if the script should display plots
FilterType (str): {'uKF', 'EKF', 'OEKF', 'SEKF', 'SuKF'}
simTime (float): The length of the simulation time
"""
# Create simulation variable names
simTaskName = "simTask"
simProcessName = "simProcess"
# Create a sim module as an empty container
scSim = SimulationBaseClass.SimBaseClass()
# set the simulation time variable used later on
simulationTime = macros.sec2nano(simTime)
#
# create the simulation process
#
dynProcess = scSim.CreateNewProcess(simProcessName)
# create the dynamics task and specify the integration update time
simulationTimeStep = macros.sec2nano(0.5)
dynProcess.addTask(scSim.CreateNewTask(simTaskName, simulationTimeStep))
#
# setup the simulation tasks/objects
#
# create sun position message at origin
sunMsgData = messaging.SpicePlanetStateMsgPayload()
sunMsg = messaging.SpicePlanetStateMsg().write(sunMsgData)
sunLog = sunMsg.recorder()
scSim.AddModelToTask(simTaskName, sunLog)
# initialize spacecraft object and set properties
scObject = spacecraft.Spacecraft()
scObject.ModelTag = "bsk-Sat"
# define the simulation inertia
I = [900., 0., 0.,
0., 800., 0.,
0., 0., 600.]
scObject.hub.mHub = 750.0 # kg - spacecraft mass
scObject.hub.r_BcB_B = [[0.0], [0.0], [0.0]] # m - position vector of body-fixed point B relative to CM
scObject.hub.IHubPntBc_B = unitTestSupport.np2EigenMatrix3d(I)
#
# set initial spacecraft states
#
scObject.hub.r_CN_NInit = [[-om.AU*1000.], [0.0], [0.0]] # m - r_CN_N
scObject.hub.v_CN_NInit = [[0.0], [0.0], [0.0]] # m/s - v_CN_N
scObject.hub.sigma_BNInit = [[0.0], [0.0], [0.]] # sigma_BN_B
scObject.hub.omega_BN_BInit = [[-0.1*macros.D2R], [0.5*macros.D2R], [0.5*macros.D2R]] # rad/s - omega_BN_B
# add spacecraft object to the simulation process
scSim.AddModelToTask(simTaskName, scObject)
dataLog = scObject.scStateOutMsg.recorder()
scSim.AddModelToTask(simTaskName, dataLog)
# Make a CSS constelation
cssConstelation = coarseSunSensor.CSSConstellation()
CSSOrientationList = [
[0.70710678118654746, -0.5, 0.5],
[0.70710678118654746, -0.5, -0.5],
[0.70710678118654746, 0.5, -0.5],
[0.70710678118654746, 0.5, 0.5],
[-0.70710678118654746, 0, 0.70710678118654757],
[-0.70710678118654746, 0.70710678118654757, 0.0],
[-0.70710678118654746, 0, -0.70710678118654757],
[-0.70710678118654746, -0.70710678118654757, 0.0]
]
counter = 0
def setupCSS(CSS):
CSS.minOutput = 0.
CSS.senNoiseStd = 0.017
CSS.sunInMsg.subscribeTo(sunMsg)
CSS.stateInMsg.subscribeTo(scObject.scStateOutMsg)
CSS.this.disown()
for CSSHat in CSSOrientationList:
newCSS = coarseSunSensor.CoarseSunSensor()
newCSS.ModelTag = "CSS" + str(counter)
counter += 1
setupCSS(newCSS)
newCSS.nHat_B = CSSHat
cssConstelation.appendCSS(newCSS)
scSim.AddModelToTask(simTaskName, cssConstelation)
#
# add the FSW CSS information
#
cssConstVehicle = messaging.CSSConfigMsgPayload()
totalCSSList = []
for CSSHat in CSSOrientationList:
newCSS = messaging.CSSUnitConfigMsgPayload()
newCSS.nHat_B = CSSHat
newCSS.CBias = 1.0
totalCSSList.append(newCSS)
cssConstVehicle.nCSS = len(CSSOrientationList)
cssConstVehicle.cssVals = totalCSSList
cssConstMsg = messaging.CSSConfigMsg().write(cssConstVehicle)
#
# Setup filter
#
numStates = 6
bVecLogger = None
if FilterType == 'EKF':
module = sunlineEKF.sunlineEKF()
module.ModelTag = "SunlineEKF"
setupEKFData(module)
# Add test module to runtime call list
scSim.AddModelToTask(simTaskName, module)
if FilterType == 'OEKF':
numStates = 3
module = okeefeEKF.okeefeEKF()
module.ModelTag = "okeefeEKF"
setupOEKFData(module)
# Add test module to runtime call list
scSim.AddModelToTask(simTaskName, module)
if FilterType == 'uKF':
module = sunlineUKF.sunlineUKF()
module.ModelTag = "SunlineUKF"
setupUKFData(module)
# Add test module to runtime call list
scSim.AddModelToTask(simTaskName, module)
if FilterType == 'SEKF':
numStates = 5
module = sunlineSEKF.sunlineSEKF()
module.ModelTag = "SunlineSEKF"
setupSEKFData(module)
# Add test module to runtime call list
scSim.AddModelToTask(simTaskName, module)
bVecLogger = module.logger('bVec_B', simulationTimeStep)
scSim.AddModelToTask(simTaskName, bVecLogger)
if FilterType == 'SuKF':
numStates = 6
module = sunlineSuKF.sunlineSuKF()
module.ModelTag = "SunlineSuKF"
setupSuKFData(module)
# Add test module to runtime call list
scSim.AddModelToTask(simTaskName, module)
bVecLogger = module.logger('bVec_B', simulationTimeStep)
scSim.AddModelToTask(simTaskName, bVecLogger)
module.cssDataInMsg.subscribeTo(cssConstelation.constellationOutMsg)
module.cssConfigInMsg.subscribeTo(cssConstMsg)
navLog = module.navStateOutMsg.recorder()
filtLog = module.filtDataOutMsg.recorder()
scSim.AddModelToTask(simTaskName, navLog)
scSim.AddModelToTask(simTaskName, filtLog)
#
# initialize Simulation
#
scSim.InitializeSimulation()
#
# configure a simulation stop time and execute the simulation run
#
scSim.ConfigureStopTime(simulationTime)
# Time the runs for performance comparisons
scSim.ExecuteSimulation()
#
# retrieve the logged data
#
def addTimeColumn(time, data):
return np.transpose(np.vstack([[time], np.transpose(data)]))
# Get messages that will make true data
timeAxis = dataLog.times()
OutSunPos = addTimeColumn(timeAxis, sunLog.PositionVector)
Outr_BN_N = addTimeColumn(timeAxis, dataLog.r_BN_N)
OutSigma_BN = addTimeColumn(timeAxis, dataLog.sigma_BN)
Outomega_BN = addTimeColumn(timeAxis, dataLog.omega_BN_B)
# Get the filter outputs through the messages
stateLog = addTimeColumn(timeAxis, filtLog.state[:, range(numStates)])
postFitLog = addTimeColumn(timeAxis, filtLog.postFitRes[:, :8])
covarLog = addTimeColumn(timeAxis, filtLog.covar[:, range(numStates*numStates)])
obsLog = addTimeColumn(timeAxis, filtLog.numObs)
# Get bVec_B through the variable logger
bVecLog = None if bVecLogger is None else addTimeColumn(timeAxis, bVecLogger.bVec_B)
dcmLog = np.zeros([len(stateLog[:,0]),3,3])
omegaExp = np.zeros([len(stateLog[:,0]),3])
if FilterType == 'SEKF':
dcm = sunlineSEKF.new_doubleArray(3 * 3)
for j in range(9):
sunlineSEKF.doubleArray_setitem(dcm, j, 0)
for i in range(len(stateLog[:,0])):
sunlineSEKF.sunlineSEKFComputeDCM_BS(stateLog[i,1:4].tolist(), bVecLog[i, 1:4].tolist(), dcm)
dcmOut = []
for j in range(9):
dcmOut.append(sunlineSEKF.doubleArray_getitem(dcm, j))
dcmLog[i,:,:] = np.array(dcmOut).reshape([3,3])
omegaExp[i,:] = -np.dot(dcmLog[i,:,:], np.array([0, stateLog[i,4], stateLog[i,5]]))
if FilterType == 'SuKF':
dcm = sunlineSuKF.new_doubleArray(3 * 3)
for j in range(9):
sunlineSuKF.doubleArray_setitem(dcm, j, 0)
for i in range(len(stateLog[:,0])):
sunlineSuKF.sunlineSuKFComputeDCM_BS(stateLog[i,1:4].tolist(), bVecLog[i, 1:4].tolist(), dcm)
dcmOut = []
for j in range(9):
dcmOut.append(sunlineSuKF.doubleArray_getitem(dcm, j))
dcmLog[i,:,:] = np.array(dcmOut).reshape([3,3])
omegaExp[i,:] = np.dot(dcmLog[i,:,:].T,Outomega_BN[i,1:])
sHat_B = np.zeros(np.shape(OutSunPos))
sHatDot_B = np.zeros(np.shape(OutSunPos))
for i in range(len(OutSunPos[:,0])):
sHat_N = (OutSunPos[i,1:] - Outr_BN_N[i,1:])/np.linalg.norm(OutSunPos[i,1:] - Outr_BN_N[i,1:])
dcm_BN = rbk.MRP2C(OutSigma_BN[i,1:])
sHat_B[i,0] = sHatDot_B[i,0]= OutSunPos[i,0]
sHat_B[i,1:] = np.dot(dcm_BN, sHat_N)
sHatDot_B[i,1:] = - np.cross(Outomega_BN[i,1:], sHat_B[i,1:] )
expected = np.zeros(np.shape(stateLog))
expected[:,0:4] = sHat_B
# The OEKF has fewer states
if FilterType != 'OEKF' and FilterType != 'SEKF' and FilterType != 'SuKF':
expected[:, 4:] = sHatDot_B[:,1:]
if FilterType == 'SEKF' or FilterType == 'SuKF':
for i in range(len(stateLog[:, 0])):
expected[i, 4] = omegaExp[i,1]
expected[i, 5] = omegaExp[i,2]
# plot the results
#
errorVsTruth = np.copy(stateLog)
errorVsTruth[:,1:] -= expected[:,1:]
Fplot.StateErrorCovarPlot(errorVsTruth, covarLog, FilterType, show_plots, saveFigures)
Fplot.StatesVsExpected(stateLog, covarLog, expected, FilterType, show_plots, saveFigures)
Fplot.PostFitResiduals(postFitLog, np.sqrt(module.qObsVal), FilterType, show_plots, saveFigures)
Fplot.numMeasurements(obsLog, FilterType, show_plots, saveFigures)
if show_plots:
plt.show()
# close the plots being saved off to avoid over-writing old and new figures
plt.close("all")
# each test method requires a single assert method to be called
# this check below just makes sure no sub-test failures were found
return
#
# This statement below ensures that the unit test scrip can be run as a
# stand-along python script
#
if __name__ == "__main__":
run(False, # save figures to file
True, # show_plots
'SuKF',
400
)