Source code for test_headingSuKF

''' '''
'''
 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 math

import matplotlib.pyplot as plt
import numpy as np
from Basilisk.architecture import messaging
from Basilisk.fswAlgorithms import headingSuKF
from Basilisk.utilities import SimulationBaseClass, macros

import headingSuKF_test_utilities as FilterPlots


def setupFilterData(filterObject):

    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, 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]

    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()

[docs] def test_functions_ukf(show_plots): """ **Validation Test Description** This subtest runs through the general modules file for square root and unscented filters. These methods include LU decompositions, QR decompositions that only provide the R matrix, as well as L, U inverses, and Cholesky decompositions. **Description of Variables Being Tested** Each method of the general module file for square root and unscented filters are tested to machine precision with errors of 1E-14 **General Documentation Comments** This is a similar test used to other filter modules """ [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): """ **Validation Test Description** The StatePropSunLine subtest runs the filter and creates synthetic measurements to trigger the measurement update method. This is tested in two parts. The filter first stabilizes to a value, and then the value is abruptly changed in order for the filter to snap back to the solution. Measurements are provided every 10 seconds which provides the sparse data that is usually characteristic of OpNav. The StateUpdateSunLine subtest runs the filter without measurements to only trigger the time update method. This ensures the filter stays at true values if no measurements are provided. **Description of Variables Being Tested** For the propagation: The state output by the filter is tested compared to the commanded target, and the covariance is ensured to converge. These are both tested to 1E-1 because of noise introduced in the measurements. The measurement updated state output by the filter is tested compared to the expected target. The stability of the state is tested to 1E-10. """ [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): __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 = headingSuKF.headingSuKF() module.ModelTag = "headingSuKF" # Add test module to runtime call list unitTestSim.AddModelToTask(unitTaskName, module) setupFilterData(module) dataLog = module.filtDataOutMsg.recorder() unitTestSim.AddModelToTask(unitTaskName, dataLog) testVector = np.array([0.9, 0.1, 0.02]) testOmega = np.array([0.01, 0.05, 0.001]) inputData = messaging.OpNavMsgPayload() opnavDataInMsg = messaging.OpNavMsg() stateTarget = testVector.tolist() inputData.r_BN_B = stateTarget stateTarget.extend([0.0, 0.0]) module.stateInit = [1., 0.2, 0.1, 0.01, 0.001] # setup message connections module.opnavDataInMsg.subscribeTo(opnavDataInMsg) unitTestSim.InitializeSimulation() t1 = 1000 for i in range(t1): if i > 0 and i%20 == 0: inputData.timeTag = macros.sec2nano(i * 0.5) inputData.valid = 1 inputData.r_BN_B += np.random.normal(0, 0.001, 3) inputData.covar_B = [0.0001**2, 0, 0, 0, 0.0001**2, 0, 0, 0, 0.0001**2] opnavDataInMsg.write(inputData, unitTestSim.TotalSim.CurrentNanos) unitTestSim.ConfigureStopTime(macros.sec2nano((i+1)*0.5)) unitTestSim.ExecuteSimulation() stateLog = dataLog.state postFitLog = dataLog.postFitRes covarLog = dataLog.covar 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+i] - covarLog[0, i*5+i]/10) > 1E-1): testFailCount += 1 testMessages.append("Covariance update failure") if(abs(stateLog[-1, i] - stateTarget[i-1]) > 1.0E-1): print(abs(stateLog[-1, i] - stateTarget[i-1])) 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) inputData.valid = 1 inputData.covar_B = [0.0001**2,0,0,0,0.0001**2,0,0,0,0.0001**2] opnavDataInMsg.write(inputData, unitTestSim.TotalSim.CurrentNanos) unitTestSim.ConfigureStopTime(macros.sec2nano((i+t1 +1)*0.5)) unitTestSim.ExecuteSimulation() stateLog = dataLog.state stateErrorLog = dataLog.stateError postFitLog = dataLog.postFitRes covarLog = dataLog.covar stateTarget[:3] = (-testVector[:3]/np.linalg.norm(testVector[:3])).tolist() for i in range(5): if(np.abs(covarLog[2*t1-10, i*5+i] - covarLog[0, i*5+i]/10)>1E-1): testFailCount += 1 testMessages.append("Covariance update failure") if(abs(stateLog[-1, i] - stateTarget[i-1]) > 1.0E-1): print(abs(stateLog[-1, i] - stateTarget[i-1])) testFailCount += 1 testMessages.append("State update failure") FilterPlots.StateCovarPlot(dataLog.times(), stateLog, covarLog, 'Update', show_plots) FilterPlots.StateCovarPlot(dataLog.times(),stateErrorLog, covarLog, 'Update_Error', show_plots) FilterPlots.PostFitResiduals(dataLog.times(), postFitLog, 0.001, 'Update', show_plots) # print out success message if no error were found if testFailCount == 0: print("PASSED: " + module.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): __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 = headingSuKF.headingSuKF() module.ModelTag = "headingSuKF" # Add test module to runtime call list unitTestSim.AddModelToTask(unitTaskName, module) setupFilterData(module) dataLog = module.filtDataOutMsg.recorder() unitTestSim.AddModelToTask(unitTaskName, dataLog) inData = messaging.OpNavMsgPayload() inDataMsg = messaging.OpNavMsg().write(inData) # setup message connections module.opnavDataInMsg.subscribeTo(inDataMsg) unitTestSim.InitializeSimulation() unitTestSim.ConfigureStopTime(macros.sec2nano(8000.0)) unitTestSim.ExecuteSimulation() stateLog = dataLog.state postFitLog = dataLog.postFitRes covarLog = dataLog.covar FilterPlots.StateCovarPlot(dataLog.times(), stateLog, covarLog, 'Prop', show_plots) FilterPlots.PostFitResiduals(dataLog.times(), postFitLog, module.qObsVal, 'Prop', show_plots) for i in range(5): if(abs(stateLog[-1, i] - stateLog[0, i]) > 1.0E-10): print(abs(stateLog[-1, i] - stateLog[0, i])) 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") # 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) # StatePropSunLine(True)