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# ISC License
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# Copyright (c) 2022, Autonomous Vehicle Systems Lab, University of Colorado Boulder
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import matplotlib.pyplot as plt
import numpy as np
import pytest
from Basilisk.architecture import messaging
from Basilisk.simulation import hingedRigidBodyMotorSensor
from Basilisk.utilities import SimulationBaseClass
from Basilisk.utilities import macros
from Basilisk.utilities import unitTestSupport
[docs]@pytest.mark.parametrize("thetaNoiseStd, thetaDotNoiseStd, accuracy", [(0.0, 0.0, 1.0e-12)])
@pytest.mark.parametrize("thetaBias, thetaDotBias",[(0,0), (-.1,.1), (.2,-.01)])
@pytest.mark.parametrize("thetaLSB, thetaDotLSB",[(-1,-1), (.1,.05), (.01,.005)])
@pytest.mark.parametrize("trueTheta, trueThetaDot",[(1.01,-0.23), (-.229,.112)])
def test_hingedRigidBodyMotorSensor(show_plots, thetaNoiseStd, thetaDotNoiseStd, thetaBias, thetaDotBias, thetaLSB, thetaDotLSB, trueTheta, trueThetaDot, accuracy):
r"""
**Validation Test Description**
This test checks the sensor's addition of a bias and discretization to the input value.
**Test Parameters**
Args:
thetaNoiseStd (double): standard deviation of the added noise to theta
thetaDotNoiseStd (double): standard deviation of the added noise to thetaDot
thetaBias (double): bias added to theta
thetaDotBias (double): bias added to thetaDot
thetaLSB (double): least significant bit for discretizing theta. Negative means no discretization.
thetaDotLSB (double): least significant bit for discretizing thetaDot. Negative means no discretization.
trueTheta (double): true value of theta
trueThetaDot (double): true value of thetaDot
accuracy (double): absolute accuracy value used in the validation tests
**Description of Variables Being Tested**
The python evaluated sensed value is compared against the module output.
"""
[testResults, testMessage] = hingedRigidBodyMotorSensorTestFunction(show_plots, thetaNoiseStd, thetaDotNoiseStd, thetaBias, thetaDotBias, thetaLSB, thetaDotLSB, trueTheta, trueThetaDot, accuracy)
assert testResults < 1, testMessage
[docs]def hingedRigidBodyMotorSensorTestFunction(show_plots, thetaNoiseStd, thetaDotNoiseStd, thetaBias, thetaDotBias, thetaLSB, thetaDotLSB, trueTheta, trueThetaDot, accuracy):
"""Test method"""
testFailCount = 0
testMessages = []
unitTaskName = "unitTask"
unitProcessName = "TestProcess"
timeStep = 0.5
totalTime = 10.0
unitTestSim = SimulationBaseClass.SimBaseClass()
testProcessRate = macros.sec2nano(timeStep)
testProc = unitTestSim.CreateNewProcess(unitProcessName)
testProc.addTask(unitTestSim.CreateNewTask(unitTaskName, testProcessRate))
# setup module to be tested
module = hingedRigidBodyMotorSensor.HingedRigidBodyMotorSensor()
module.ModelTag = "hingedRigidBodyMotorSensorTag"
unitTestSim.AddModelToTask(unitTaskName, module)
# Configure blank module input messages
hingedRigidBodyMotorSensorInMsgData = messaging.HingedRigidBodyMsgPayload()
# set up fake input message
hingedRigidBodyMotorSensorInMsgData.theta = trueTheta;
hingedRigidBodyMotorSensorInMsgData.thetaDot = trueThetaDot;
hingedRigidBodyMotorSensorInMsg = messaging.HingedRigidBodyMsg().write(hingedRigidBodyMotorSensorInMsgData)
# subscribe input messages to module
module.hingedRigidBodyMotorSensorInMsg.subscribeTo(hingedRigidBodyMotorSensorInMsg)
# set up output message recorder objects
dataLog = module.hingedRigidBodyMotorSensorOutMsg.recorder()
unitTestSim.AddModelToTask(unitTaskName, dataLog)
# set up variables in sensor
module.thetaNoiseStd = thetaNoiseStd
module.thetaDotNoiseStd = thetaDotNoiseStd
module.thetaBias = thetaBias
module.thetaDotBias = thetaDotBias
module.thetaLSB = thetaLSB
module.thetaDotLSB = thetaDotLSB
module.setRNGSeed(2) # change RNG seed here
unitTestSim.InitializeSimulation()
unitTestSim.ConfigureStopTime(macros.sec2nano(totalTime))
unitTestSim.ExecuteSimulation()
# pull module data and make sure it is correct
sensedTheta = dataLog.theta[-1]
sensedThetaDot = dataLog.thetaDot[-1]
# add bias to test values
biasTheta = trueTheta+thetaBias
biasThetaDot = trueThetaDot+thetaDotBias
# discretize test values
if thetaLSB > 0:
discTheta = round(biasTheta/thetaLSB)*thetaLSB
else:
discTheta = biasTheta
if thetaDotLSB > 0:
discThetaDot = round(biasThetaDot/thetaDotLSB)*thetaDotLSB
else:
discThetaDot = biasThetaDot
print(sensedTheta)
print(sensedThetaDot)
print(trueTheta)
print(trueThetaDot)
# check adding bias
if abs(thetaBias) > accuracy:
if not unitTestSupport.isDoubleEqual(sensedTheta, discTheta, accuracy):
testMessages.append("Failed theta bias.")
testFailCount += 1
if not unitTestSupport.isDoubleEqual(sensedThetaDot, discThetaDot, accuracy):
testMessages.append("Failed thetaDot bias.")
testFailCount += 1
# check discretization
if abs(thetaLSB) > accuracy:
if not unitTestSupport.isDoubleEqual(sensedTheta, discTheta, accuracy):
testMessages.append("Failed theta discretization.")
testFailCount += 1
if not unitTestSupport.isDoubleEqual(sensedThetaDot, discThetaDot, accuracy):
testMessages.append("Failed thetaDot discretization.")
testFailCount += 1
if testFailCount == 0:
print("PASSED: " + module.ModelTag)
else:
print(testMessages)
if show_plots:
thetaVals = trueTheta*np.ones(int(totalTime/timeStep)+1)
thetaDotVals = trueThetaDot*np.ones(int(totalTime/timeStep)+1)
timeAxis = dataLog.times() * macros.NANO2SEC
plt.figure(1)
plt.plot(timeAxis, thetaVals,
label=r'Theta Truth')
plt.plot(timeAxis, dataLog.theta,
label=r'Theta Sensed')
plt.legend(loc='lower right')
plt.xlabel('Time [s]')
plt.ylabel('Theta [rad]')
plt.figure(2)
plt.plot(timeAxis, thetaDotVals,
label=r'ThetaDot Truth')
plt.plot(timeAxis, dataLog.thetaDot,
label=r'ThetaDot Sensed')
plt.legend(loc='lower right')
plt.xlabel('Time [s]')
plt.ylabel('ThetaDot [rad]')
plt.show()
plt.close("all")
return [testFailCount, "".join(testMessages)]
if __name__ == "__main__":
test_hingedRigidBodyMotorSensor(
True,
0.1, 0.05, # noise: note this makes bias/disc tests FAIL
0.0, 0.0, # bias
-1, -1, # discretization
1.01, -0.23 , # true values
1.0e-12) # accuracy