''' '''
'''
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 os
import inspect # Don't worry about this, standard stuff plus file discovery
filename = inspect.getframeinfo(inspect.currentframe()).filename
path = os.path.dirname(os.path.abspath(filename))
# bskName = 'Basilisk'
# splitPath = path.split(bskName)
# bskPath = splitPath[0] + '/' + bskName + '/'
# sys.path.append(bskPath + 'modules')
# sys.path.append(bskPath + 'PythonModules')
from Basilisk import __path__
bskPath = __path__[0]
from Basilisk.utilities.MonteCarlo.Controller import Controller, RetentionPolicy
from Basilisk.utilities.MonteCarlo.Dispersions import UniformEulerAngleMRPDispersion, UniformDispersion, NormalVectorCartDispersion, OrbitalElementDispersion
# import simulation related support
from Basilisk.simulation import spacecraftPlus
from Basilisk.utilities import orbitalMotion
from Basilisk.utilities import simIncludeGravBody
from Basilisk.utilities import macros
from Basilisk.utilities import SimulationBaseClass
import shutil
import matplotlib.pyplot as plt
from mpl_toolkits import mplot3d
import numpy as np
import pytest
NUMBER_OF_RUNS = 4
VERBOSE = True
PROCESSES = 2
[docs]def myCreationFunction():
''' function that returns a simulation '''
# Create a sim module as an empty container
sim = SimulationBaseClass.SimBaseClass()
# Create simulation variable names
simTaskName = "simTask"
simProcessName = "simProcess"
# Create the simulation process
dynProcess = sim.CreateNewProcess(simProcessName)
# Create the dynamics task and specify the integration update time
simulationTimeStep = macros.sec2nano(10.)
dynProcess.addTask(sim.CreateNewTask(simTaskName, simulationTimeStep))
# Setup the simulation modules
# Initialize spacecraftPlus object and set properties
scObject = spacecraftPlus.SpacecraftPlus()
scObject.ModelTag = "spacecraftBody"
# Add spacecraftPlus object to the simulation process
sim.AddModelToTask(simTaskName, scObject)
# Setup Earth gravity body and attach gravity model to spaceCraftPlus
gravFactory = simIncludeGravBody.gravBodyFactory()
planet = gravFactory.createEarth()
planet.isCentralBody = True
planet.useSphericalHarmParams = True
simIncludeGravBody.loadGravFromFile(bskPath + '/supportData/LocalGravData/GGM03S-J2-only.txt'
, planet.spherHarm
, 2
)
scObject.gravField.gravBodies = spacecraftPlus.GravBodyVector([planet])
# Setup the orbit using classical orbit elements
oe = orbitalMotion.ClassicElements()
rGEO = 42000. * 1000 # meters
oe.a = rGEO
oe.e = 0.00001
oe.i = 0.0 * macros.D2R
oe.Omega = 48.2 * macros.D2R
oe.omega = 347.8 * macros.D2R
oe.f = 85.3 * macros.D2R
rN, vN = orbitalMotion.elem2rv(planet.mu, oe) # this stores consistent initial orbit elements
oe = orbitalMotion.rv2elem(planet.mu, rN, vN) # with circular or equatorial orbit, some angles are arbitrary
# initialize Spacecraft States with the initialization variables
scObject.hub.r_CN_NInit = rN # m - r_BN_N
scObject.hub.v_CN_NInit = vN # m/s - v_BN_N
# set the simulation time
mean_motion = np.sqrt(planet.mu / oe.a / oe.a / oe.a)
period = 2. * np.pi / mean_motion
simulationTime = macros.sec2nano(period / 4)
# configure a simulation stop time time and execute the simulation run
sim.ConfigureStopTime(simulationTime)
return sim
[docs]def myExecutionFunction(sim):
''' function that executes a simulation '''
sim.InitializeSimulationAndDiscover()
sim.ExecuteSimulation()
retainedMessageName = "inertial_state_output"
retainedRate = macros.sec2nano(10)
var1 = "v_BN_N"
var2 = "r_BN_N"
dataType1 = list(range(3))
dataType2 = list(range(3))
colorList = ['b', 'r', 'g', 'k']
def myDataCallback(monteCarloData, retentionPolicy):
data = np.array(monteCarloData["messages"]["inertial_state_output.r_BN_N"])
plt.plot(data[:, 1], data[:, 2], colorList[monteCarloData["index"]], label="run " + str(monteCarloData["index"]))
plt.xlabel('X-coordinate')
plt.ylabel('Y-coordinate')
plt.legend()
@pytest.mark.slowtest
def test_MonteCarloSimulation(show_plots):
# Test a montecarlo simulation
dirName = os.path.abspath(os.path.dirname(__file__)) + "/tmp_montecarlo_test"
monteCarlo = Controller()
monteCarlo.setShouldDisperseSeeds(True)
monteCarlo.setExecutionFunction(myExecutionFunction)
monteCarlo.setSimulationFunction(myCreationFunction)
monteCarlo.setExecutionCount(NUMBER_OF_RUNS)
monteCarlo.setThreadCount(PROCESSES)
monteCarlo.setVerbose(True)
monteCarlo.setArchiveDir(dirName)
# Add some dispersions
disp1Name = 'TaskList[0].TaskModels[0].hub.sigma_BNInit'
disp2Name = 'TaskList[0].TaskModels[0].hub.omega_BN_BInit'
disp3Name = 'TaskList[0].TaskModels[0].hub.mHub'
disp4Name = 'TaskList[0].TaskModels[0].hub.r_BcB_B'
disp5Name = 'TaskList[0].TaskModels[0].hub.r_CN_NInit'
disp6Name = 'TaskList[0].TaskModels[0].hub.v_CN_NInit'
dispDict = {}
dispDict["mu"] = 0.3986004415E+15
dispDict["a"] = ["normal", 42000 * 1E3, 2000 * 1E3]
dispDict["e"] = ["uniform", 0, 0.5]
dispDict["i"] = ["uniform", -80, 80]
dispDict["Omega"] = None
dispDict["omega"] = ["uniform", 80, 90]
dispDict["f"] = ["uniform", 0, 359]
monteCarlo.addDispersion(OrbitalElementDispersion(disp5Name, disp6Name, dispDict))
monteCarlo.addDispersion(UniformEulerAngleMRPDispersion(disp1Name))
monteCarlo.addDispersion(NormalVectorCartDispersion(disp2Name, 0.0, 0.75 / 3.0 * np.pi / 180))
monteCarlo.addDispersion(UniformDispersion(disp3Name, ([1300.0 - 812.3, 1500.0 - 812.3])))
monteCarlo.addDispersion(
NormalVectorCartDispersion(disp4Name, [0.0, 0.0, 1.0], [0.05 / 3.0, 0.05 / 3.0, 0.1 / 3.0]))
# Add retention policy
retentionPolicy = RetentionPolicy()
retentionPolicy.addMessageLog(retainedMessageName, [(var1, dataType1), (var2, dataType2)], retainedRate)
retentionPolicy.setDataCallback(myDataCallback)
monteCarlo.addRetentionPolicy(retentionPolicy)
failures = monteCarlo.executeSimulations()
assert len(failures) == 0, "No runs should fail"
# Test loading data from runs from disk
monteCarloLoaded = Controller.load(dirName)
retainedData = monteCarloLoaded.getRetainedData(NUMBER_OF_RUNS-1)
assert retainedData is not None, "Retained data should be available after execution"
assert "messages" in retainedData, "Retained data should retain messages"
assert "inertial_state_output.r_BN_N" in retainedData["messages"], "Retained messages should exist"
assert "inertial_state_output.v_BN_N" in retainedData["messages"], "Retained messages should exist"
# rerun the case and it should be the same, because we dispersed random seeds
oldOutput = retainedData["messages"]["inertial_state_output.r_BN_N"]
failed = monteCarloLoaded.reRunCases([NUMBER_OF_RUNS-1])
assert len(failed) == 0, "Should rerun case successfully"
retainedData = monteCarloLoaded.getRetainedData(NUMBER_OF_RUNS-1)
newOutput = retainedData["messages"]["inertial_state_output.r_BN_N"]
for k1, v1 in enumerate(oldOutput):
for k2, v2 in enumerate(v1):
assert np.fabs(oldOutput[k1][k2] - newOutput[k1][k2]) < .001, \
"Outputs shouldn't change on runs if random seeds are same"
# test the initial parameters were saved from runs, and they differ between runs
params1 = monteCarloLoaded.getParameters(NUMBER_OF_RUNS-1)
params2 = monteCarloLoaded.getParameters(NUMBER_OF_RUNS-2)
assert "TaskList[0].TaskModels[0].RNGSeed" in params1, "random number seed should be applied"
for dispName in [disp1Name, disp2Name, disp3Name, disp4Name]:
assert dispName in params1, "dispersion should be applied"
# assert two different runs had different parameters.
assert params1[dispName] != params2[dispName], "dispersion should be different in each run"
monteCarloLoaded.executeCallbacks()
if show_plots:
plt.show()
shutil.rmtree(dirName)
assert not os.path.exists(dirName), "No leftover data should exist after the test"
if __name__ == "__main__":
test_MonteCarloSimulation(show_plots=True)