''' '''
'''
ISC License
Copyright (c) 2016-2018, 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 sys, os, inspect
import numpy as np
import pytest
import math
from Basilisk.utilities import SimulationBaseClass, macros, unitTestSupport
from Basilisk.simulation import coarse_sun_sensor
import matplotlib.pyplot as plt
from Basilisk.fswAlgorithms import headingSuKF, cssComm, fswMessages # import the module that is to be tested
import headingSuKF_test_utilities as FilterPlots
def setupFilterData(filterObject):
filterObject.opnavOutMsgName = "opnav_state_estimate"
filterObject.filtDataOutMsgName = "heading_filter_data"
filterObject.opnavDataInMsgName = "opnav_sensor_data"
filterObject.alpha = 0.02
filterObject.beta = 2.0
filterObject.kappa = 0.0
# filterObject.state = [0.0, 0., 0., 0., 0.]
filterObject.stateInit = [0.0, 0.0, 1.0, 0.0, 0.0]
filterObject.covarInit = [0.1, 0.0, 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, 0.001, 0.0,
0.0, 0.0, 0.0, 0.0, 0.001]
qNoiseIn = np.identity(5)
qNoiseIn[0:3, 0:3] = qNoiseIn[0:3, 0:3]*0.01*0.01
qNoiseIn[3:5, 3:5] = qNoiseIn[3:5, 3:5]*0.001*0.001
filterObject.qNoise = qNoiseIn.reshape(25).tolist()
filterObject.qObsVal = 0.001
filterObject.sensorUseThresh = 0.
[docs]def test_functions_ukf(show_plots):
"""Module Unit Test"""
[testResults, testMessage] = heading_utilities_test(show_plots)
assert testResults < 1, testMessage
# 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]def test_all_heading_kf(show_plots):
"""Module Unit Test"""
[testResults, testMessage] = StatePropSunLine(show_plots)
assert testResults < 1, testMessage
[testResults, testMessage] = StateUpdateSunLine(show_plots)
assert testResults < 1, testMessage
def heading_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 = headingSuKF.new_doubleArray(len(AMatrix))
AVector = headingSuKF.new_doubleArray(len(AMatrix))
for i in range(len(AMatrix)):
headingSuKF.doubleArray_setitem(AVector, i, AMatrix[i])
headingSuKF.doubleArray_setitem(RVector, i, 0.0)
headingSuKF.ukfQRDJustR(AVector, 6, 4, RVector)
RMatrix = []
for i in range(4*4):
RMatrix.append(headingSuKF.doubleArray_getitem(RVector, i))
RBaseNumpy = np.array(RMatrix).reshape(4,4)
AMatNumpy = np.array(AMatrix).reshape(6,4)
q,r = np.linalg.qr(AMatNumpy)
for i in range(r.shape[0]):
if r[i,i] < 0.0:
r[i,:] *= -1.0
if np.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 = headingSuKF.new_doubleArray(len(AMatrix))
AVector = headingSuKF.new_doubleArray(len(AMatrix))
for i in range(len(AMatrix)):
headingSuKF.doubleArray_setitem(AVector, i, AMatrix[i])
headingSuKF.doubleArray_setitem(RVector, i, 0.0)
headingSuKF.ukfQRDJustR(AVector, 5, 4, RVector)
RMatrix = []
for i in range(4*4):
RMatrix.append(headingSuKF.doubleArray_getitem(RVector, i))
RBaseNumpy = np.array(RMatrix).reshape(4,4)
AMatNumpy = np.array(AMatrix).reshape(5,4)
q,r = np.linalg.qr(AMatNumpy)
for i in range(r.shape[0]):
if r[i,i] < 0.0:
r[i,:] *= -1.0
if np.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 = headingSuKF.new_doubleArray(len(AMatrix))
AVector = headingSuKF.new_doubleArray(len(AMatrix))
for i in range(len(AMatrix)):
headingSuKF.doubleArray_setitem(AVector, i, AMatrix[i])
headingSuKF.doubleArray_setitem(RVector, i, 0.0)
headingSuKF.ukfQRDJustR(AVector, 6, 2, RVector)
RMatrix = []
for i in range(2*2):
RMatrix.append(headingSuKF.doubleArray_getitem(RVector, i))
RBaseNumpy = np.array(RMatrix).reshape(2,2)
AMatNumpy = np.array(AMatrix).reshape(6,2)
q,r = np.linalg.qr(AMatNumpy)
for i in range(r.shape[0]):
if r[i,i] < 0.0:
r[i,:] *= -1.0
if np.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 = headingSuKF.new_doubleArray(len(LUSourceMat))
LVector = headingSuKF.new_doubleArray(len(LUSourceMat))
UVector = headingSuKF.new_doubleArray(len(LUSourceMat))
intSwapVector = headingSuKF.new_intArray(3)
for i in range(len(LUSourceMat)):
headingSuKF.doubleArray_setitem(LUSVector, i, LUSourceMat[i])
headingSuKF.doubleArray_setitem(UVector, i, 0.0)
headingSuKF.doubleArray_setitem(LVector, i, 0.0)
exCount = headingSuKF.ukfLUD(LUSVector, 3, 3, LVector, intSwapVector)
#headingSuKF.ukfUInv(LUSVector, 3, 3, UVector)
LMatrix = []
UMatrix = []
#UMatrix = []
for i in range(3):
currRow = headingSuKF.intArray_getitem(intSwapVector, i)
for j in range(3):
if(j<i):
LMatrix.append(headingSuKF.doubleArray_getitem(LVector, i*3+j))
UMatrix.append(0.0)
elif(j>i):
LMatrix.append(0.0)
UMatrix.append(headingSuKF.doubleArray_getitem(LVector, i*3+j))
else:
LMatrix.append(1.0)
UMatrix.append(headingSuKF.doubleArray_getitem(LVector, i*3+j))
# UMatrix.append(headingSuKF.doubleArray_getitem(UVector, i))
LMatrix = np.array(LMatrix).reshape(3,3)
UMatrix = np.array(UMatrix).reshape(3,3)
outMat = np.dot(LMatrix, UMatrix)
outMatSwap = np.zeros((3,3))
for i in range(3):
currRow = headingSuKF.intArray_getitem(intSwapVector, i)
outMatSwap[i,:] = outMat[currRow, :]
outMat[currRow,:] = outMat[i, :]
LuSourceArray = np.array(LUSourceMat).reshape(3,3)
if(np.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 = headingSuKF.new_doubleArray(len(EqnSourceMat))
EqnBVector = headingSuKF.new_doubleArray(len(LUSourceMat)//3)
EqnOutVector = headingSuKF.new_doubleArray(len(LUSourceMat)//3)
for i in range(len(EqnSourceMat)):
headingSuKF.doubleArray_setitem(EqnVector, i, EqnSourceMat[i])
headingSuKF.doubleArray_setitem(EqnBVector, i//3, BVector[i//3])
headingSuKF.intArray_setitem(intSwapVector, i//3, 0)
headingSuKF.doubleArray_setitem(LVector, i, 0.0)
exCount = headingSuKF.ukfLUD(EqnVector, 3, 3, LVector, intSwapVector)
headingSuKF.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(headingSuKF.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 = headingSuKF.new_doubleArray(len(InvSourceMat))
InvVector = headingSuKF.new_doubleArray(len(InvSourceMat))
for i in range(len(InvSourceMat)):
headingSuKF.doubleArray_setitem(SourceVector, i, InvSourceMat[i])
headingSuKF.doubleArray_setitem(InvVector, i, 0.0)
nRow = int(math.sqrt(len(InvSourceMat)))
headingSuKF.ukfMatInv(SourceVector, nRow, nRow, InvVector)
InvOut = []
for i in range(len(InvSourceMat)):
InvOut.append(headingSuKF.doubleArray_getitem(InvVector, i))
InvOut = np.array(InvOut).reshape(nRow, nRow)
expectIdent = np.dot(InvOut, np.array(InvSourceMat).reshape(3,3))
errorNorm = np.linalg.norm(expectIdent - np.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 = headingSuKF.new_doubleArray(len(cholTestMat))
CholVector = headingSuKF.new_doubleArray(len(cholTestMat))
for i in range(len(cholTestMat)):
headingSuKF.doubleArray_setitem(SourceVector, i, cholTestMat[i])
headingSuKF.doubleArray_setitem(CholVector, i, 0.0)
nRow = int(math.sqrt(len(cholTestMat)))
headingSuKF.ukfCholDecomp(SourceVector, nRow, nRow, CholVector)
cholOut = []
for i in range(len(cholTestMat)):
cholOut.append(headingSuKF.doubleArray_getitem(CholVector, i))
cholOut = np.array(cholOut).reshape(nRow, nRow)
cholComp = np.linalg.cholesky(np.array(cholTestMat).reshape(nRow, nRow))
errorNorm = np.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 = headingSuKF.new_doubleArray(len(InvSourceMat))
InvVector = headingSuKF.new_doubleArray(len(InvSourceMat))
for i in range(len(InvSourceMat)):
headingSuKF.doubleArray_setitem(SourceVector, i, InvSourceMat[i])
headingSuKF.doubleArray_setitem(InvVector, i, 0.0)
nRow = int(math.sqrt(len(InvSourceMat)))
headingSuKF.ukfLInv(SourceVector, nRow, nRow, InvVector)
InvOut = []
for i in range(len(InvSourceMat)):
InvOut.append(headingSuKF.doubleArray_getitem(InvVector, i))
InvOut = np.array(InvOut).reshape(nRow, nRow)
expectIdent = np.dot(InvOut, np.array(InvSourceMat).reshape(nRow,nRow))
errorNorm = np.linalg.norm(expectIdent - np.identity(nRow))
if(errorNorm > 1.0E-12):
print(errorNorm)
testFailCount += 1
testMessages.append("L Matrix Inverse accuracy failure")
InvSourceMat = np.transpose(np.array(InvSourceMat).reshape(nRow, nRow)).reshape(nRow*nRow).tolist()
SourceVector = headingSuKF.new_doubleArray(len(InvSourceMat))
InvVector = headingSuKF.new_doubleArray(len(InvSourceMat))
for i in range(len(InvSourceMat)):
headingSuKF.doubleArray_setitem(SourceVector, i, InvSourceMat[i])
headingSuKF.doubleArray_setitem(InvVector, i, 0.0)
nRow = int(math.sqrt(len(InvSourceMat)))
headingSuKF.ukfUInv(SourceVector, nRow, nRow, InvVector)
InvOut = []
for i in range(len(InvSourceMat)):
InvOut.append(headingSuKF.doubleArray_getitem(InvVector, i))
InvOut = np.array(InvOut).reshape(nRow, nRow)
expectIdent = np.dot(InvOut, np.array(InvSourceMat).reshape(nRow,nRow))
errorNorm = np.linalg.norm(expectIdent - np.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()
# print out success message if no error were found
if testFailCount == 0:
print("PASSED: " + " UKF utilities")
# return fail count and join into a single string all messages in the list
# testMessage
return [testFailCount, ''.join(testMessages)]
def StateUpdateSunLine(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
moduleConfig = headingSuKF.HeadingSuKFConfig()
moduleWrap = unitTestSim.setModelDataWrap(moduleConfig)
moduleWrap.ModelTag = "headingSuKF"
# Add test module to runtime call list
unitTestSim.AddModelToTask(unitTaskName, moduleWrap, moduleConfig)
setupFilterData(moduleConfig)
unitTestSim.TotalSim.logThisMessage('heading_filter_data', testProcessRate)
testVector = np.array([0.9, 0.1, 0.02])
testOmega = np.array([0.01, 0.05, 0.001])
inputData = headingSuKF.OpNavFswMsg()
inputMessageSize = inputData.getStructSize()
unitTestSim.TotalSim.CreateNewMessage(unitProcessName,
moduleConfig.opnavDataInMsgName,
inputMessageSize,
2) # number of buffers (leave at 2 as default, don't make zero)
stateTarget = testVector.tolist()
inputData.r_BN_B = stateTarget
stateTarget.extend([0.0, 0.0])
moduleConfig.stateInit = [1., 0.2, 0.1, 0.01, 0.001]
unitTestSim.InitializeSimulation()
t1 = 1000
for i in range(t1):
if i > 0 and i%20 == 0:
inputData.timeTag = macros.sec2nano(i * 0.5)
inputData.r_BN_B += np.random.normal(0, 0.001, 3)
unitTestSim.TotalSim.WriteMessageData(moduleConfig.opnavDataInMsgName,
inputMessageSize,
unitTestSim.TotalSim.CurrentNanos,
inputData)
unitTestSim.ConfigureStopTime(macros.sec2nano((i+1)*0.5))
unitTestSim.ExecuteSimulation()
stateLog = unitTestSim.pullMessageLogData('heading_filter_data' + ".state", list(range(5)))
postFitLog = unitTestSim.pullMessageLogData('heading_filter_data' + ".postFitRes", list(range(3)))
covarLog = unitTestSim.pullMessageLogData('heading_filter_data' + ".covar", list(range(5*5)))
stateTarget[:3] = (-testVector[:3]/np.linalg.norm(testVector[:3])).tolist()
for i in range(5):
# check covariance immediately after measurement is taken,
# ensure order of magnitude less than initial covariance.
if(np.abs(covarLog[t1-10, i*5+1+i] - covarLog[0, i*5+1+i]/10)>1E-1):
testFailCount += 1
testMessages.append("Covariance update failure")
if(abs(stateLog[-1, i+1] - stateTarget[i]) > 1.0E-1):
print(abs(stateLog[-1, i+1] - stateTarget[i]))
testFailCount += 1
testMessages.append("State update failure")
testVector = np.array([0.6, -0.1, 0.2])
stateTarget = testVector.tolist()
inputData.r_BN_B = stateTarget
stateTarget.extend([0.0, 0.0])
for i in range(t1):
if i%20 == 0:
inputData.timeTag = macros.sec2nano(i*0.5 +t1 +1)
inputData.r_BN_B += np.random.normal(0, 0.001, 3)
unitTestSim.TotalSim.WriteMessageData(moduleConfig.opnavDataInMsgName,
inputMessageSize,
unitTestSim.TotalSim.CurrentNanos,
inputData)
unitTestSim.ConfigureStopTime(macros.sec2nano((i+t1 +1)*0.5))
unitTestSim.ExecuteSimulation()
stateLog = unitTestSim.pullMessageLogData('heading_filter_data' + ".state", list(range(5)))
stateErrorLog = unitTestSim.pullMessageLogData('heading_filter_data' + ".stateError", list(range(5)))
postFitLog = unitTestSim.pullMessageLogData('heading_filter_data' + ".postFitRes", list(range(3)))
covarLog = unitTestSim.pullMessageLogData('heading_filter_data' + ".covar", list(range(5*5)))
stateTarget[:3] = (-testVector[:3]/np.linalg.norm(testVector[:3])).tolist()
for i in range(5):
if(np.abs(covarLog[2*t1-10, i*5+1+i] - covarLog[0, i*5+1+i]/10)>1E-1):
testFailCount += 1
testMessages.append("Covariance update failure")
if(abs(stateLog[-1, i+1] - stateTarget[i]) > 1.0E-1):
print(abs(stateLog[-1, i+1] - stateTarget[i]))
print("here")
testFailCount += 1
testMessages.append("State update failure")
FilterPlots.StateCovarPlot(stateLog, covarLog, 'Update', show_plots)
FilterPlots.StateCovarPlot(stateErrorLog, covarLog, 'Update_Error', show_plots)
FilterPlots.PostFitResiduals(postFitLog, moduleConfig.qObsVal, 'Update', show_plots)
# print out success message if no error were found
if testFailCount == 0:
print("PASSED: " + moduleWrap.ModelTag + " state update")
# return fail count and join into a single string all messages in the list
# testMessage
return [testFailCount, ''.join(testMessages)]
def StatePropSunLine(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
moduleConfig = headingSuKF.HeadingSuKFConfig()
moduleWrap = unitTestSim.setModelDataWrap(moduleConfig)
moduleWrap.ModelTag = "headingSuKF"
# Add test module to runtime call list
unitTestSim.AddModelToTask(unitTaskName, moduleWrap, moduleConfig)
setupFilterData(moduleConfig)
unitTestSim.TotalSim.logThisMessage('heading_filter_data', testProcessRate)
unitTestSim.InitializeSimulation()
unitTestSim.ConfigureStopTime(macros.sec2nano(8000.0))
unitTestSim.ExecuteSimulation()
stateLog = unitTestSim.pullMessageLogData('heading_filter_data' + ".state", list(range(5)))
postFitLog = unitTestSim.pullMessageLogData('heading_filter_data' + ".postFitRes", list(range(3)))
covarLog = unitTestSim.pullMessageLogData('heading_filter_data' + ".covar", list(range(5*5)))
FilterPlots.StateCovarPlot(stateLog, covarLog, 'Prop', show_plots)
FilterPlots.PostFitResiduals(postFitLog, moduleConfig.qObsVal, 'Prop', show_plots)
for i in range(5):
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: " + moduleWrap.ModelTag + " state propagation")
# return fail count and join into a single string all messages in the list
# testMessage
return [testFailCount, ''.join(testMessages)]
if __name__ == "__main__":
# test_all_heading_kf(True)
StateUpdateSunLine(True)