#
# 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.
#
import math
import matplotlib.pyplot as plt
import numpy
import pytest
from Basilisk.architecture import messaging
from Basilisk.fswAlgorithms import sunlineUKF
from Basilisk.utilities import SimulationBaseClass, macros
import SunLineuKF_test_utilities as FilterPlots
def addTimeColumn(time, data):
return numpy.transpose(numpy.vstack([[time], numpy.transpose(data)]))
def setupFilterData(filterObject):
filterObject.alpha = 0.02
filterObject.beta = 2.0
filterObject.kappa = 0.0
filterObject.state = [1.0, 0.0, 0.0, 0.0, 0.0, 0.0]
filterObject.covar = [0.4, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.4, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.4, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.04, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.04, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.04]
qNoiseIn = numpy.identity(6)
qNoiseIn[0:3, 0:3] = qNoiseIn[0:3, 0:3]*0.01*0.01
qNoiseIn[3:6, 3:6] = qNoiseIn[3:6, 3:6]*0.001*0.001
filterObject.qNoise = qNoiseIn.reshape(36).tolist()
filterObject.qObsVal = 0.001
# uncomment this line is this test is to be skipped in the global unit test run, adjust message as needed
# @pytest.mark.skipif(conditionstring)
# uncomment this line if this test has an expected failure, adjust message as needed
# @pytest.mark.xfail() # need to update how the RW states are defined
# provide a unique test method name, starting with test_
[docs]@pytest.mark.parametrize("function", ["sunline_utilities_test"
, "checkStatePropSunLine"
, "checkStateUpdateSunLine"
])
def test_all_sunline_kf(show_plots, function):
"""Module Unit Test"""
[testResults, testMessage] = eval(function + '(show_plots)')
assert testResults < 1, testMessage
def sunline_utilities_test(show_plots):
# The __tracebackhide__ setting influences pytest showing of tracebacks:
# the mrp_steering_tracking() function will not be shown unless the
# --fulltrace command line option is specified.
__tracebackhide__ = True
testFailCount = 0 # zero unit test result counter
testMessages = [] # create empty list to store test log messages
# Initialize the test module configuration data
AMatrix = [0.488894, 0.888396, 0.325191, 0.319207,
1.03469, -1.14707, -0.754928, 0.312859,
0.726885, -1.06887, 1.3703, -0.86488,
-0.303441, -0.809499, -1.71152, -0.0300513,
0.293871, -2.94428, -0.102242, -0.164879,
-0.787283, 1.43838, -0.241447, 0.627707]
RVector = sunlineUKF.new_doubleArray(len(AMatrix))
AVector = sunlineUKF.new_doubleArray(len(AMatrix))
for i in range(len(AMatrix)):
sunlineUKF.doubleArray_setitem(AVector, i, AMatrix[i])
sunlineUKF.doubleArray_setitem(RVector, i, 0.0)
sunlineUKF.ukfQRDJustR(AVector, 6, 4, RVector)
RMatrix = []
for i in range(4*4):
RMatrix.append(sunlineUKF.doubleArray_getitem(RVector, i))
RBaseNumpy = numpy.array(RMatrix).reshape(4,4)
AMatNumpy = numpy.array(AMatrix).reshape(6,4)
q,r = numpy.linalg.qr(AMatNumpy)
for i in range(r.shape[0]):
if r[i,i] < 0.0:
r[i,:] *= -1.0
if numpy.linalg.norm(r - RBaseNumpy) > 1.0E-15:
testFailCount += 1
testMessages.append("QR Decomposition accuracy failure")
AMatrix = [1.09327, 1.10927, -0.863653, 1.32288,
-1.21412, -1.1135, -0.00684933, -2.43508,
-0.769666, 0.371379, -0.225584, -1.76492,
-1.08906, 0.0325575, 0.552527, -1.6256,
1.54421, 0.0859311, -1.49159, 1.59683]
RVector = sunlineUKF.new_doubleArray(len(AMatrix))
AVector = sunlineUKF.new_doubleArray(len(AMatrix))
for i in range(len(AMatrix)):
sunlineUKF.doubleArray_setitem(AVector, i, AMatrix[i])
sunlineUKF.doubleArray_setitem(RVector, i, 0.0)
sunlineUKF.ukfQRDJustR(AVector, 5, 4, RVector)
RMatrix = []
for i in range(4*4):
RMatrix.append(sunlineUKF.doubleArray_getitem(RVector, i))
RBaseNumpy = numpy.array(RMatrix).reshape(4,4)
AMatNumpy = numpy.array(AMatrix).reshape(5,4)
q,r = numpy.linalg.qr(AMatNumpy)
for i in range(r.shape[0]):
if r[i,i] < 0.0:
r[i,:] *= -1.0
if numpy.linalg.norm(r - RBaseNumpy) > 1.0E-14:
testFailCount += 1
testMessages.append("QR Decomposition accuracy failure")
AMatrix = [ 0.2236, 0,
0, 0.2236,
-0.2236, 0,
0, -0.2236,
0.0170, 0,
0, 0.0170]
RVector = sunlineUKF.new_doubleArray(len(AMatrix))
AVector = sunlineUKF.new_doubleArray(len(AMatrix))
for i in range(len(AMatrix)):
sunlineUKF.doubleArray_setitem(AVector, i, AMatrix[i])
sunlineUKF.doubleArray_setitem(RVector, i, 0.0)
sunlineUKF.ukfQRDJustR(AVector, 6, 2, RVector)
RMatrix = []
for i in range(2*2):
RMatrix.append(sunlineUKF.doubleArray_getitem(RVector, i))
RBaseNumpy = numpy.array(RMatrix).reshape(2,2)
AMatNumpy = numpy.array(AMatrix).reshape(6,2)
q,r = numpy.linalg.qr(AMatNumpy)
for i in range(r.shape[0]):
if r[i,i] < 0.0:
r[i,:] *= -1.0
if numpy.linalg.norm(r - RBaseNumpy) > 1.0E-15:
testFailCount += 1
testMessages.append("QR Decomposition accuracy failure")
LUSourceMat = [8,1,6,3,5,7,4,9,2]
LUSVector = sunlineUKF.new_doubleArray(len(LUSourceMat))
LVector = sunlineUKF.new_doubleArray(len(LUSourceMat))
UVector = sunlineUKF.new_doubleArray(len(LUSourceMat))
intSwapVector = sunlineUKF.new_intArray(3)
for i in range(len(LUSourceMat)):
sunlineUKF.doubleArray_setitem(LUSVector, i, LUSourceMat[i])
sunlineUKF.doubleArray_setitem(UVector, i, 0.0)
sunlineUKF.doubleArray_setitem(LVector, i, 0.0)
exCount = sunlineUKF.ukfLUD(LUSVector, 3, 3, LVector, intSwapVector)
#sunlineUKF.ukfUInv(LUSVector, 3, 3, UVector)
LMatrix = []
UMatrix = []
#UMatrix = []
for i in range(3):
currRow = sunlineUKF.intArray_getitem(intSwapVector, i)
for j in range(3):
if(j<i):
LMatrix.append(sunlineUKF.doubleArray_getitem(LVector, i*3+j))
UMatrix.append(0.0)
elif(j>i):
LMatrix.append(0.0)
UMatrix.append(sunlineUKF.doubleArray_getitem(LVector, i*3+j))
else:
LMatrix.append(1.0)
UMatrix.append(sunlineUKF.doubleArray_getitem(LVector, i*3+j))
# UMatrix.append(sunlineUKF.doubleArray_getitem(UVector, i))
LMatrix = numpy.array(LMatrix).reshape(3,3)
UMatrix = numpy.array(UMatrix).reshape(3,3)
outMat = numpy.dot(LMatrix, UMatrix)
outMatSwap = numpy.zeros((3,3))
for i in range(3):
currRow = sunlineUKF.intArray_getitem(intSwapVector, i)
outMatSwap[i,:] = outMat[currRow, :]
outMat[currRow,:] = outMat[i, :]
LuSourceArray = numpy.array(LUSourceMat).reshape(3,3)
if(numpy.linalg.norm(outMatSwap - LuSourceArray) > 1.0E-14):
testFailCount += 1
testMessages.append("LU Decomposition accuracy failure")
EqnSourceMat = [2.0, 1.0, 3.0, 2.0, 6.0, 8.0, 6.0, 8.0, 18.0]
BVector = [1.0, 3.0, 5.0]
EqnVector = sunlineUKF.new_doubleArray(len(EqnSourceMat))
EqnBVector = sunlineUKF.new_doubleArray(len(LUSourceMat)//3)
EqnOutVector = sunlineUKF.new_doubleArray(len(LUSourceMat)//3)
for i in range(len(EqnSourceMat)):
sunlineUKF.doubleArray_setitem(EqnVector, i, EqnSourceMat[i])
sunlineUKF.doubleArray_setitem(EqnBVector, i//3, BVector[i//3])
sunlineUKF.intArray_setitem(intSwapVector, i//3, 0)
sunlineUKF.doubleArray_setitem(LVector, i, 0.0)
exCount = sunlineUKF.ukfLUD(EqnVector, 3, 3, LVector, intSwapVector)
sunlineUKF.ukfLUBckSlv(LVector, 3, 3, intSwapVector, EqnBVector, EqnOutVector)
expectedSol = [3.0/10.0, 4.0/10.0, 0.0]
errorVal = 0.0
for i in range(3):
errorVal += abs(sunlineUKF.doubleArray_getitem(EqnOutVector, i) -expectedSol[i])
if(errorVal > 1.0E-14):
testFailCount += 1
testMessages.append("LU Back-Solve accuracy failure")
InvSourceMat = [8,1,6,3,5,7,4,9,2]
SourceVector = sunlineUKF.new_doubleArray(len(InvSourceMat))
InvVector = sunlineUKF.new_doubleArray(len(InvSourceMat))
for i in range(len(InvSourceMat)):
sunlineUKF.doubleArray_setitem(SourceVector, i, InvSourceMat[i])
sunlineUKF.doubleArray_setitem(InvVector, i, 0.0)
nRow = int(math.sqrt(len(InvSourceMat)))
sunlineUKF.ukfMatInv(SourceVector, nRow, nRow, InvVector)
InvOut = []
for i in range(len(InvSourceMat)):
InvOut.append(sunlineUKF.doubleArray_getitem(InvVector, i))
InvOut = numpy.array(InvOut).reshape(nRow, nRow)
expectIdent = numpy.dot(InvOut, numpy.array(InvSourceMat).reshape(3,3))
errorNorm = numpy.linalg.norm(expectIdent - numpy.identity(3))
if(errorNorm > 1.0E-14):
testFailCount += 1
testMessages.append("LU Matrix Inverse accuracy failure")
cholTestMat = [1.0, 0.0, 0.0, 0.0, 10.0, 5.0, 0.0, 5.0, 10.0]
SourceVector = sunlineUKF.new_doubleArray(len(cholTestMat))
CholVector = sunlineUKF.new_doubleArray(len(cholTestMat))
for i in range(len(cholTestMat)):
sunlineUKF.doubleArray_setitem(SourceVector, i, cholTestMat[i])
sunlineUKF.doubleArray_setitem(CholVector, i, 0.0)
nRow = int(math.sqrt(len(cholTestMat)))
sunlineUKF.ukfCholDecomp(SourceVector, nRow, nRow, CholVector)
cholOut = []
for i in range(len(cholTestMat)):
cholOut.append(sunlineUKF.doubleArray_getitem(CholVector, i))
cholOut = numpy.array(cholOut).reshape(nRow, nRow)
cholComp = numpy.linalg.cholesky(numpy.array(cholTestMat).reshape(nRow, nRow))
errorNorm = numpy.linalg.norm(cholOut - cholComp)
if(errorNorm > 1.0E-14):
testFailCount += 1
testMessages.append("Cholesky Matrix Decomposition accuracy failure")
InvSourceMat = [2.1950926119414667, 0.0, 0.0, 0.0,
1.0974804773131115, 1.9010439702743847, 0.0, 0.0,
0.0, 1.2672359635912551, 1.7923572711881284, 0.0,
1.0974804773131113, -0.63357997864171967, 1.7920348101787789, 0.033997451205364251]
SourceVector = sunlineUKF.new_doubleArray(len(InvSourceMat))
InvVector = sunlineUKF.new_doubleArray(len(InvSourceMat))
for i in range(len(InvSourceMat)):
sunlineUKF.doubleArray_setitem(SourceVector, i, InvSourceMat[i])
sunlineUKF.doubleArray_setitem(InvVector, i, 0.0)
nRow = int(math.sqrt(len(InvSourceMat)))
sunlineUKF.ukfLInv(SourceVector, nRow, nRow, InvVector)
InvOut = []
for i in range(len(InvSourceMat)):
InvOut.append(sunlineUKF.doubleArray_getitem(InvVector, i))
InvOut = numpy.array(InvOut).reshape(nRow, nRow)
expectIdent = numpy.dot(InvOut, numpy.array(InvSourceMat).reshape(nRow,nRow))
errorNorm = numpy.linalg.norm(expectIdent - numpy.identity(nRow))
if(errorNorm > 1.0E-12):
print(errorNorm)
testFailCount += 1
testMessages.append("L Matrix Inverse accuracy failure")
InvSourceMat = numpy.transpose(numpy.array(InvSourceMat).reshape(nRow, nRow)).reshape(nRow*nRow).tolist()
SourceVector = sunlineUKF.new_doubleArray(len(InvSourceMat))
InvVector = sunlineUKF.new_doubleArray(len(InvSourceMat))
for i in range(len(InvSourceMat)):
sunlineUKF.doubleArray_setitem(SourceVector, i, InvSourceMat[i])
sunlineUKF.doubleArray_setitem(InvVector, i, 0.0)
nRow = int(math.sqrt(len(InvSourceMat)))
sunlineUKF.ukfUInv(SourceVector, nRow, nRow, InvVector)
InvOut = []
for i in range(len(InvSourceMat)):
InvOut.append(sunlineUKF.doubleArray_getitem(InvVector, i))
InvOut = numpy.array(InvOut).reshape(nRow, nRow)
expectIdent = numpy.dot(InvOut, numpy.array(InvSourceMat).reshape(nRow,nRow))
errorNorm = numpy.linalg.norm(expectIdent - numpy.identity(nRow))
if(errorNorm > 1.0E-12):
print(errorNorm)
testFailCount += 1
testMessages.append("U Matrix Inverse accuracy failure")
# If the argument provided at commandline "--show_plots" evaluates as true,
# plot all figures
if show_plots:
plt.show()
plt.close('all')
# print out success message if no error were found
if testFailCount == 0:
print("PASSED: " + " UKF utilities")
else:
print(testMessages)
# return fail count and join into a single string all messages in the list
# testMessage
return [testFailCount, ''.join(testMessages)]
def checkStateUpdateSunLine(show_plots):
# The __tracebackhide__ setting influences pytest showing of tracebacks:
# the mrp_steering_tracking() function will not be shown unless the
# --fulltrace command line option is specified.
__tracebackhide__ = True
testFailCount = 0 # zero unit test result counter
testMessages = [] # create empty list to store test log messages
unitTaskName = "unitTask" # arbitrary name (don't change)
unitProcessName = "TestProcess" # arbitrary name (don't change)
# Create a sim module as an empty container
unitTestSim = SimulationBaseClass.SimBaseClass()
# Create test thread
testProcessRate = macros.sec2nano(0.5) # update process rate update time
testProc = unitTestSim.CreateNewProcess(unitProcessName)
testProc.addTask(unitTestSim.CreateNewTask(unitTaskName, testProcessRate))
# Construct algorithm and associated C++ container
module = sunlineUKF.sunlineUKF()
module.ModelTag = "SunlineUKF"
# Add test module to runtime call list
unitTestSim.AddModelToTask(unitTaskName, module)
setupFilterData(module)
cssConstelation = messaging.CSSConfigMsgPayload()
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],
]
totalCSSList = []
for CSSHat in CSSOrientationList:
newCSS = messaging.CSSUnitConfigMsgPayload()
newCSS.CBias = 1.0
newCSS.nHat_B = CSSHat
totalCSSList.append(newCSS)
cssConstelation.nCSS = len(CSSOrientationList)
cssConstelation.cssVals = totalCSSList
cssConstInMsg = messaging.CSSConfigMsg().write(cssConstelation)
testVector = numpy.array([-0.7, 0.7, 0.0])
inputData = messaging.CSSArraySensorMsgPayload()
dotList = []
for element in CSSOrientationList:
dotProd = numpy.dot(numpy.array(element), testVector)
dotList.append(dotProd)
inputData.CosValue = dotList
cssDataInMsg = messaging.CSSArraySensorMsg()
stateTarget = testVector.tolist()
stateTarget.extend([0.0, 0.0, 0.0])
module.state = [0.7, 0.7, 0.0]
dataLog = module.filtDataOutMsg.recorder()
unitTestSim.AddModelToTask(unitTaskName, dataLog)
# connect messages
module.cssDataInMsg.subscribeTo(cssDataInMsg)
module.cssConfigInMsg.subscribeTo(cssConstInMsg)
unitTestSim.InitializeSimulation()
for i in range(400):
if i > 20:
cssDataInMsg.write(inputData, unitTestSim.TotalSim.CurrentNanos)
unitTestSim.ConfigureStopTime(macros.sec2nano((i+1)*0.5))
unitTestSim.ExecuteSimulation()
stateLog = addTimeColumn(dataLog.times(), dataLog.state)
postFitLog = addTimeColumn(dataLog.times(), dataLog.postFitRes)
covarLog = addTimeColumn(dataLog.times(), dataLog.covar)
for i in range(6):
if(covarLog[-1, i*6+1+i] > covarLog[0, i*6+1+i]/100):
testFailCount += 1
testMessages.append("Covariance update failure")
if(abs(stateLog[-1, i+1] - stateTarget[i]) > 1.0E-5):
print(abs(stateLog[-1, i+1] - stateTarget[i]))
testFailCount += 1
testMessages.append("State update failure")
testVector = numpy.array([-0.8, -0.9, 0.0])
inputData = messaging.CSSArraySensorMsgPayload()
dotList = []
for element in CSSOrientationList:
dotProd = numpy.dot(numpy.array(element), testVector)
dotList.append(dotProd)
inputData.CosValue = dotList
for i in range(400):
if i > 20:
cssDataInMsg.write(inputData, unitTestSim.TotalSim.CurrentNanos)
unitTestSim.ConfigureStopTime(macros.sec2nano((i+401)*0.5))
unitTestSim.ExecuteSimulation()
stateLog = addTimeColumn(dataLog.times(), dataLog.state)
postFitLog = addTimeColumn(dataLog.times(), dataLog.postFitRes)
covarLog = addTimeColumn(dataLog.times(), dataLog.covar)
stateTarget = testVector.tolist()
stateTarget.extend([0.0, 0.0, 0.0])
for i in range(6):
if(covarLog[-1, i*6+1+i] > covarLog[0, i*6+1+i]/100):
testFailCount += 1
testMessages.append("Covariance update failure")
if(abs(stateLog[-1, i+1] - stateTarget[i]) > 1.0E-5):
print(abs(stateLog[-1, i+1] - stateTarget[i]))
testFailCount += 1
testMessages.append("State update failure")
FilterPlots.StateCovarPlot(stateLog, covarLog, 'update', show_plots)
FilterPlots.PostFitResiduals(postFitLog, module.qObsVal, 'update', show_plots)
# print out success message if no error were found
if testFailCount == 0:
print("PASSED: " + module.ModelTag + " state update")
else:
print(testMessages)
# return fail count and join into a single string all messages in the list
# testMessage
return [testFailCount, ''.join(testMessages)]
def checkStatePropSunLine(show_plots):
# The __tracebackhide__ setting influences pytest showing of tracebacks:
# the mrp_steering_tracking() function will not be shown unless the
# --fulltrace command line option is specified.
__tracebackhide__ = True
testFailCount = 0 # zero unit test result counter
testMessages = [] # create empty list to store test log messages
unitTaskName = "unitTask" # arbitrary name (don't change)
unitProcessName = "TestProcess" # arbitrary name (don't change)
# Create a sim module as an empty container
unitTestSim = SimulationBaseClass.SimBaseClass()
# Create test thread
testProcessRate = macros.sec2nano(0.5) # update process rate update time
testProc = unitTestSim.CreateNewProcess(unitProcessName)
testProc.addTask(unitTestSim.CreateNewTask(unitTaskName, testProcessRate))
# Construct algorithm and associated C++ container
module = sunlineUKF.sunlineUKF()
module.ModelTag = "SunlineUKF"
# Add test module to runtime call list
unitTestSim.AddModelToTask(unitTaskName, module)
setupFilterData(module)
dataLog = module.filtDataOutMsg.recorder()
unitTestSim.AddModelToTask(unitTaskName, dataLog)
# connect messages
cssConstInMsg = messaging.CSSConfigMsg()
cssDataInMsg = messaging.CSSArraySensorMsg()
module.cssDataInMsg.subscribeTo(cssDataInMsg)
module.cssConfigInMsg.subscribeTo(cssConstInMsg)
unitTestSim.InitializeSimulation()
unitTestSim.ConfigureStopTime(macros.sec2nano(8000.0))
unitTestSim.ExecuteSimulation()
stateLog = addTimeColumn(dataLog.times(), dataLog.state)
postFitLog = addTimeColumn(dataLog.times(), dataLog.postFitRes)
covarLog = addTimeColumn(dataLog.times(), dataLog.covar)
FilterPlots.StateCovarPlot(stateLog, covarLog, 'prop', show_plots)
FilterPlots.PostFitResiduals(postFitLog, module.qObsVal, 'prop', show_plots)
for i in range(6):
if(abs(stateLog[-1, i+1] - stateLog[0, i+1]) > 1.0E-10):
print(abs(stateLog[-1, i+1] - stateLog[0, i+1]))
testFailCount += 1
testMessages.append("State propagation failure")
# print out success message if no error were found
if testFailCount == 0:
print("PASSED: " + module.ModelTag + " state propagation")
else:
print(testMessages)
# return fail count and join into a single string all messages in the list
# testMessage
return [testFailCount, ''.join(testMessages)]
if __name__ == "__main__":
test_all_sunline_kf(True)