# 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.
#
# MonteCarlo module. Please read the accompanying README.md for usage information.
#
# Purpose: This module is used to run a simulation with varying initial parameters.
# Author: Nathan Bellowe
# Creation Date: July. 20, 2017
#
import os
import random
import shutil
import sys
import traceback
import warnings
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=DeprecationWarning)
import copy
import gzip
import json
import signal
import time
import numpy as np
import multiprocessing as mp
import pickle as pickle
from Basilisk.utilities.MonteCarlo.DataWriter import DataWriter
from Basilisk.utilities.MonteCarlo.RetentionPolicy import RetentionPolicy
from Basilisk.utilities.simulationProgessBar import SimulationProgressBar
[docs]class Controller:
"""
The MonteCarloController class is used to run a monte carlo simulation.
It is used to execute multiple runs of a simulation with varying initial parameters. Data from each run is retained
in order to analyze differences in the simulation runs and the parameters used.
"""
def __init__(self):
self.executionCount = 0
self.ICrunFlag = False
self.icDirectory = ""
self.archiveDir = None
self.varCast = None
self.numProcess = mp.cpu_count()
self.simParams = SimulationParameters(
creationFunction=None,
executionFunction=None,
configureFunction=None,
retentionPolicies=[],
shouldArchiveParameters=False,
shouldDisperseSeeds=False,
dispersions=[],
filename="",
icfilename=""
)
[docs] def setShowProgressBar(self, value):
"""
To enable or disable progress bar to show simulation progress
Args:
value: boolean value, decide to show/hide progress bar
"""
self.simParams.showProgressBar = value
[docs] @staticmethod
def load(runDirectory):
"""
Load a previously completed MonteCarlo simulation
Args:
The path to the MonteCarlo.data file that contains the archived MonteCarlo run
"""
filename = os.path.abspath(runDirectory) + "/MonteCarlo.data"
with gzip.open(filename) as pickledData:
data = pickle.load(pickledData)
if data.simParams.verbose:
print("Loading montecarlo at", filename)
data.multiProcManager = mp.Manager()
data.dataOutQueue = data.multiProcManager.Queue()
data.dataWriter = DataWriter(data.dataOutQueue)
data.dataWriter.daemon = False
return data
[docs] def setExecutionFunction(self, newModule):
"""
Set an execution function that executes a simulation instance.
Args:
executionFunction: (sim: SimulationBaseClass) => None
A function with one parameter, a simulation instance.
The function will be called after the creationFunction and configurationFunction in each simulation run.
It must execute the simulation.
Its return value is not used.
"""
self.simParams.executionFunction = newModule
[docs] def setSimulationFunction(self, newObject):
"""
Set the function that creates the simulation instance.
Args:
creationFunction: () => SimulationBaseClass
A function with no parameters, that returns a simulation instance.
"""
self.simParams.creationFunction = newObject
[docs] def setShouldDisperseSeeds(self, seedDisp):
"""
Disperse the RNG seeds of each run in the MonteCarlo
Args:
seedDisp: bool
Whether to disperse the RNG seeds in each run of the simulation
"""
self.simParams.shouldDisperseSeeds = seedDisp
[docs] def setExecutionCount(self, newCount):
"""
Set the number of runs for the MonteCarlo simulation
Args:
newCount: int
The number of runs to use for the simulation
"""
self.executionCount = newCount
[docs] def addDispersion(self, disp):
"""
Add a dispersion to the simulation.
Args:
disp: Dispersion
The dispersion to add to the simulation.
"""
self.simParams.dispersions.append(disp)
[docs] def addRetentionPolicy(self, policy):
"""
Add a retention policy to the simulation.
Args:
disp: RetentionPolicy
The retention policy to add to the simulation.
This defines variables to be logged and saved
"""
self.simParams.retentionPolicies.append(policy)
[docs] def setThreadCount(self, threads):
"""
Set the number of threads to use for the monte carlo simulation
Args:
threads: int
Number of threads to execute the montecarlo run on.
"""
self.numProcess = threads
[docs] def setVerbose(self, verbose):
"""
Use verbose output for this MonteCarlo run
Args:
verbose: bool
Whether to print verbose information during this MonteCarlo sim.
"""
self.simParams.verbose = verbose
[docs] def setDispMagnitudeFile(self, magnitudes):
"""
Save .txt with the magnitude of each dispersion in % or sigma away from mean
Args:
magnitudes: bool
Whether to save extra files for analysis.
"""
self.simParams.saveDispMag = magnitudes
def setShouldArchiveParameters(self, shouldArchiveParameters):
self.simParams.shouldArchiveParameters = shouldArchiveParameters
[docs] def setArchiveDir(self, dirName):
"""
Set-up archives for this MonteCarlo run
Args:
dirName: string
The name of the directory to archive runs in.
None, if no archive desired.
"""
self.archiveDir = os.path.abspath(dirName) + "/"
self.simParams.shouldArchiveParameters = dirName is not None
self.simParams.filename = self.archiveDir
[docs] def setVarCast(self, varCast):
"""
Set the variable type to downcast the data to
:param varCast: 'float', 'integer', 'signed', 'unsigned' (see pandas.to_numeric documentation)
:return:
"""
self.varCast = varCast
[docs] def setICDir(self, dirName):
"""
Set-up archives containing IC data
Args:
dirName: string
The name of the directory to archive runs in.
None, if no archive desired.
"""
self.icDirectory = os.path.abspath(dirName) + "/"
self.simParams.shouldArchiveParameters = True
self.simParams.icfilename = self.icDirectory
[docs] def setICRunFlag(self, bool):
"""
Set the number of threads to use for the monte carlo simulation
Args:
threads: int
Number of threads to execute the montecarlo run on.
"""
self.ICrunFlag = bool
[docs] def getRetainedData(self, case):
"""
Get the data that was retained for a run, or list of runs.
Args:
cases: int The desired case to get data from.
Returns:
The retained data for that run is returned.
"""
if self.ICrunFlag:
oldRunDataFile = self.icDirectory + "run" + str(case) + ".data"
else:
oldRunDataFile = self.archiveDir + "run" + str(case) + ".data"
with gzip.open(oldRunDataFile) as pickledData:
data = pickle.load(pickledData)
return data
[docs] def getRetainedDatas(self, cases):
"""
Get the data that was retained for a list of runs.
Args:
cases: int[] The desired cases to get data from.
Returns:
A generator is returned, which will yield, in-order, the retained data for each of these cases
"""
for case in cases:
yield self.getRetainedData(case) # call this method recursively, yielding the result
[docs] def getParameters(self, caseNumber):
"""
Get the parameters used for a particular run of the montecarlo
:param caseNumber: The number of the run to get the parameters used for.
:type caseNumber: int
:return: A dictionary of the parameters of the simulation
For example:
{"keyForSim": parameterValue, 'TaskList[0].TaskModels[0].RNGSeed': 1674764759}
"""
if self.ICrunFlag:
filename = self.icDirectory + "run" + str(caseNumber) + ".json"
else:
filename = self.archiveDir + "run" + str(caseNumber) + ".json"
with open(filename, "r") as dispersionFile:
dispersions = json.load(dispersionFile)
return dispersions
[docs] def reRunCases(self, caseList):
"""
Rerun some cases from a MonteCarlo run. Does not run in parallel
Args:
caseList: int[]
The list of runs to repeat, a list of numbers.
Returns:
failures: int[]
The list of failed runs.
"""
# the list of failures
failed = []
for caseNumber in caseList:
if self.simParams.verbose:
print("Rerunning", caseNumber)
oldRunFile = self.archiveDir + "run" + str(caseNumber) + ".json"
if not os.path.exists(oldRunFile):
print("ERROR re-running case: " + oldRunFile)
continue
# use old simulation parameters, modified slightly.
simParams = copy.deepcopy(self.simParams)
simParams.index = caseNumber
# don't redisperse seeds, we want to use the ones saved in the oldRunFile
simParams.shouldDisperseSeeds = False
# don't retain any data so remove all retention policies
simParams.retentionPolicies = []
with open(oldRunFile, "r") as runParameters:
simParams.modifications = json.load(runParameters)
# execute simulation with dispersion
executor = SimulationExecutor()
success = executor([simParams, self.dataOutQueue])
if not success:
print("Error re-executing run", caseNumber)
failed.append(caseNumber)
if len(failed) > 0:
failed.sort()
print("Failed rerunning cases:", failed)
return failed
[docs] def runInitialConditions(self, caseList):
"""
Run initial conditions given in a file
Args:
caseList: int[]
The list of runs to repeat, a list of numbers.
Returns:
failures: int[]
The list of failed runs.
"""
# the list of failures
failed = []
assert self.icDirectory != "", "No initial condition directory was given"
assert self.ICrunFlag is not False, "IC run flag was not set"
if self.simParams.verbose:
print("Beginning simulation with {0} runs on {1} threads".format(self.executionCount, self.numProcess))
if self.simParams.shouldArchiveParameters:
if not os.path.exists(self.icDirectory):
print("Cannot run initial conditions: the directory given does not exist")
if self.simParams.verbose:
print("Archiving a copy of this simulation before running it in 'MonteCarlo.data'")
try:
with gzip.open(self.icDirectory + "MonteCarlo.data", "w") as pickleFile:
pickle.dump(self, pickleFile) # dump this controller object into a file.
except Exception as e:
print("Unknown exception while trying to pickle monte-carlo-controller... \ncontinuing...\n\n", e)
# Create Queue, but don't ever start it.
self.multiProcManager = mp.Manager()
self.dataOutQueue = self.multiProcManager.Queue()
self.dataWriter = DataWriter(self.dataOutQueue)
self.dataWriter.daemon = False
# If archiving the rerun data -- make sure not to delete the original data!
if self.archiveDir is not None:
if self.archiveDir != self.icDirectory:
if os.path.exists(self.archiveDir):
shutil.rmtree(self.archiveDir)
os.mkdir(self.archiveDir)
self.dataWriter.setLogDir(self.archiveDir)
self.dataWriter.start()
else:
print("ERROR: The archive directory is set as the icDirectory. Proceeding would have overwriten all data " \
"within: " + self.archiveDir + " with the select rerun cases! Exiting.\n")
sys.exit("Change the archive directory to a new location when rerunning cases.")
else:
print("No archive data specified; no data will be logged to dataframes")
jobsFinished = 0 # keep track of what simulations have finished
# The simulation executor is responsible for executing simulation given a simulation's parameters
# It is called within worker threads with each worker's simulation parameters
simulationExecutor = SimulationExecutor()
#
progressBar = SimulationProgressBar(len(caseList), self.simParams.showProgressBar)
if self.numProcess == 1: # don't make child thread
if self.simParams.verbose:
print("Executing sequentially...")
i = 0
for i in range(len(caseList)):
simGenerator = self.generateICSims(caseList[i:i+1])
for sim in simGenerator:
try:
simulationExecutor([sim, self.dataOutQueue])
except:
failed.append(i)
i += 1
progressBar.update(i)
else:
numSims = len(caseList)
if self.numProcess > numSims:
print("Fewer MCs spawned than processes assigned (%d < %d). Changing processes count to %d." % (numSims, self.numProcess, numSims))
self.numProcess = numSims
for i in range(numSims//self.numProcess):
# If number of sims doesn't factor evenly into the number of processes:
if numSims % self.numProcess != 0 and i == len(list(range(numSims//self.numProcess)))-1:
offset = numSims % self.numProcess
else:
offset = 0
simGenerator = self.generateICSims(caseList[self.numProcess*i:self.numProcess*(i+1)+offset])
pool = mp.Pool(self.numProcess)
try:
# yields results *as* the workers finish jobs
for result in pool.imap_unordered(simulationExecutor, [(x, self.dataOutQueue) for x in simGenerator]):
if result[0] is not True: # workers return True on success
failed.append(result[1]) # add failed jobs to the list of failures
print("Job", result[1], "failed...")
jobsFinished += 1
progressBar.update(jobsFinished)
pool.close()
except KeyboardInterrupt as e:
print("Ctrl-C was hit, closing pool")
# failed.extend(range(jobsFinished, numSims)) # fail all potentially running jobs...
pool.terminate()
raise e
except Exception as e:
print("Unknown exception while running simulations:", e)
# failed.extend(range(jobsFinished, numSims)) # fail all potentially running jobs...
traceback.print_exc()
pool.terminate()
finally:
pool.join()
progressBar.markComplete()
progressBar.close()
# If the data was archiving, close the queue.
if self.archiveDir is not None and self.archiveDir != self.icDirectory:
while not self.dataOutQueue.empty():
time.sleep(1)
self.dataOutQueue.put((None, None, True))
time.sleep(5)
# if there are failures
if len(failed) > 0:
failed.sort()
if self.simParams.verbose:
print("Failed", failed, "saving to 'failures.txt'")
if self.simParams.shouldArchiveParameters:
# write a file that contains log of failed runs
with open(self.icDirectory + "failures.txt", "w") as failFile:
failFile.write(str(failed))
return failed
[docs] def generateICSims(self, caseList):
"""
Generator function to clone a baseSimulation for IC run
Args:
baseSimulation: SimulationParams
A base simulation to clone.
numSims: int[]
The desired runs to generate.
Returns:
generator<SimulationParams>
A generator that yields that number of cloned simulations
"""
# make a list of simulations to execute by cloning the base-simulation and
# changing each clone's index and filename to make a list of
# simulations to execute
for caseNumber in caseList:
if self.simParams.verbose:
print("Running IC ", caseNumber)
oldRunFile = self.icDirectory + "run" + str(caseNumber) + ".json"
if not os.path.exists(oldRunFile):
print("ERROR running IC case: " + oldRunFile)
continue
# use old simulation parameters, modified slightly.
simParams = copy.deepcopy(self.simParams)
simParams.index = caseNumber
# don't redisperse seeds, we want to use the ones saved in the oldRunFile
simParams.shouldDisperseSeeds = False
simParams.icfilename = self.icDirectory + "run" + str(caseNumber)
with open(oldRunFile, "r") as runParameters:
simParams.modifications = json.load(runParameters)
yield simParams
[docs] def generateSims(self, simNumList):
"""
Generator function to clone a baseSimulation
Args:
baseSimulation: SimulationParams
A base simulation to clone.
numSims: int[]
The desired runs to generate.
Returns:
generator<SimulationParams>
A generator that yields that number of cloned simulations
"""
# make a list of simulations to execute by cloning the base-simulation and
# changing each clone's index and filename to make a list of
# simulations to execute
for i in simNumList:
simClone = copy.deepcopy(self.simParams)
simClone.index = i
simClone.filename += "run" + str(i)
yield simClone
[docs] def executeCallbacks(self, rng=None, retentionPolicies=[]):
"""
Execute retention policy callbacks after running a monteCarlo sim.
Args:
rng: A list of simulations to execute callbacks on
retentionPolicies: the retention policies to execute
"""
if rng is None:
rng = list(range(self.executionCount))
if retentionPolicies == []:
retentionPolicies = self.simParams.retentionPolicies
for simIndex in rng:
data = self.getRetainedData(simIndex)
for retentionPolicy in retentionPolicies:
retentionPolicy.executeCallback(data)
[docs] def executeSimulations(self):
"""
Execute simulations in parallel
:return: failed: int[]
A list of the indices of all failed simulation runs.
"""
if self.simParams.verbose:
print("Beginning simulation with {0} runs on {1} threads".format(self.executionCount, self.numProcess))
if self.simParams.shouldArchiveParameters:
if os.path.exists(self.archiveDir):
shutil.rmtree(self.archiveDir, ignore_errors=True)
os.mkdir(self.archiveDir)
if self.simParams.verbose:
print("Archiving a copy of this simulation before running it in 'MonteCarlo.data'")
try:
with gzip.open(self.archiveDir + "MonteCarlo.data", "wb") as pickleFile:
pickle.dump(self, pickleFile) # dump this controller object into a file.
except Exception as e:
print("Unknown exception while trying to pickle monte-carlo-controller... \ncontinuing...\n\n", e)
self.multiProcManager = mp.Manager()
self.dataOutQueue = self.multiProcManager.Queue()
self.dataWriter = DataWriter(self.dataOutQueue)
self.dataWriter.daemon = False
numSims = self.executionCount
# start data writer process
self.dataWriter.setLogDir(self.archiveDir)
self.dataWriter.setVarCast(self.varCast)
self.dataWriter.start()
# Avoid building a full list of all simulations to run in memory,
# instead only generating simulations right before they are needed by a waiting worker
# This is accomplished using a generator and pool.imap, -- simulations are only built
# when they are about to be passed to a worker, avoiding memory overhead of first building simulations
# There is a system-dependent chunking behavior, sometimes 10-20 are generated at a time.
# simGenerator = self.generateSims(range(numSims))
failed = [] # keep track of the indices of failed simulations
jobsFinished = 0 # keep track of what simulations have finished
# The simulation executor is responsible for executing simulation given a simulation's parameters
# It is called within worker threads with each worker's simulation parameters
simulationExecutor = SimulationExecutor()
progressBar = SimulationProgressBar(numSims, self.simParams.showProgressBar)
# The outermost for-loop for both the serial and multiprocessed sim generator is not necessary. It
# is a temporary fix to a memory leak which is assumed to be a result of the simGenerator not collecting
# garbage properly. # TODO: Find a more permenant solution to the leak.
if self.numProcess == 1: # don't make child thread
if self.simParams.verbose:
print("Executing sequentially...")
i = 0
for i in range(numSims):
simGenerator = self.generateSims(list(range(i,i+1)))
for sim in simGenerator:
try:
run_ok = simulationExecutor([sim, self.dataOutQueue])[0]
except:
failed.append(i)
else:
if not run_ok:
failed.append(i)
i += 1
progressBar.update(i)
else:
if self.numProcess > numSims:
print("Fewer MCs spawned than processes assigned (%d < %d). Changing processes count to %d." % (numSims, self.numProcess, numSims))
self.numProcess = numSims
for i in range(numSims//self.numProcess):
# If number of sims doesn't factor evenly into the number of processes:
if numSims % self.numProcess != 0 and i == len(list(range(numSims//self.numProcess)))-1:
offset = numSims % self.numProcess
else:
offset = 0
simGenerator = self.generateSims(list(range(self.numProcess*i, self.numProcess*(i+1)+offset)))
pool = mp.Pool(self.numProcess)
try:
# yields results *as* the workers finish jobs
for result in pool.imap_unordered(simulationExecutor, [(x, self.dataOutQueue) for x in simGenerator]):
if result[0] is not True: # workers return True on success
failed.append(result[1]) # add failed jobs to the list of failures
print("Job", result[1], "failed...")
jobsFinished += 1
progressBar.update(jobsFinished)
pool.close()
except KeyboardInterrupt as e:
print("Ctrl-C was hit, closing pool")
failed.extend(list(range(jobsFinished, numSims))) # fail all potentially running jobs...
pool.terminate()
raise e
except Exception as e:
print("Unknown exception while running simulations:", e)
failed.extend(list(range(jobsFinished, numSims))) # fail all potentially running jobs...
traceback.print_exc()
pool.terminate()
finally:
# Wait until all data is logged from the spawned runs before proceeding with the next set.
pool.join()
progressBar.markComplete()
progressBar.close()
# Wait until all data logging is finished before concatenation dataframes and shutting down the pool
while not self.dataOutQueue.empty():
time.sleep(1)
self.dataOutQueue.put((None, None, True))
time.sleep(5)
# if there are failures
if len(failed) > 0:
failed.sort()
if self.simParams.verbose:
print("Failed", failed, "saving to 'failures.txt'")
if self.simParams.shouldArchiveParameters:
# write a file that contains log of failed runs
with open(self.archiveDir + "failures.txt", "w") as failFile:
failFile.write(str(failed))
return failed
[docs]class SimulationParameters():
"""
This class represents the run parameters for a simulation, with information including
- a function that creates the simulation
- a function that executes the simulation
- the dispersions to use on that simulation
- parameters describing the data to be retained for a simulation
- whether randomized seeds should be applied to the simulation
- whether data should be archived
"""
def __init__(self, creationFunction, executionFunction, configureFunction,
retentionPolicies, dispersions, shouldDisperseSeeds,
shouldArchiveParameters, filename, icfilename, index=None, verbose=False, modifications={},
showProgressBar=False):
self.index = index
self.creationFunction = creationFunction
self.executionFunction = executionFunction
self.configureFunction = configureFunction
self.retentionPolicies = retentionPolicies
self.dispersions = dispersions
self.shouldDisperseSeeds = shouldDisperseSeeds
self.shouldArchiveParameters = shouldArchiveParameters
self.filename = filename
self.icfilename = icfilename
self.verbose = verbose
self.modifications = modifications
self.dispersionMag = {}
self.saveDispMag = False
self.showProgressBar = showProgressBar
[docs]class SimulationExecutor:
"""
This class is used to execute a simulation in a worker thread.
To use, create an instance of this class, and then call the instance with the simulation parameters to run them in::
executor = SimulationExecutor()
simParams = SimulationParameters()
successFlag = executor(simParams)
This class can be used to execute a simulation on a different thread, by using this class as the processes target.
"""
#
@classmethod
def __call__(cls, params):
"""
In each worker process, we execute this function (by calling this object)
Args:
params [simParams, data out queue]:
A SimulationParameters object for the simulation to be executed and the output data queue
for the data writer.
Returns:
success: bool
(True, simParams.index) if simulation run was successful
(False, simParams.index) if simulation run was unsuccessful
"""
simParams = params[0]
dataOutQueue = params[1]
try:
signal.signal(signal.SIGINT, signal.SIG_IGN) # On ctrl-c ignore the signal... let the parent deal with it.
# must make new random seed on each new thread.
np.random.seed(simParams.index * 10)
random.seed(simParams.index * 10)
# create the users sim by calling their supplied creationFunction
simInstance = simParams.creationFunction()
# build a list of the parameter and random seed modifications to make
modifications = simParams.modifications
magnitudes = simParams.dispersionMag
# we may want to disperse random seeds
if simParams.shouldDisperseSeeds:
# generate the random seeds for the model (but don't apply them yet)
# Note: This sets the RNGSeeds before all other modifications
randomSeedDispersions = cls.disperseSeeds(simInstance)
for name, value in randomSeedDispersions.items():
modifications[name] = value
# used if rerunning ICs from a .json file, modifications will contain the
# RNGSeeds that need to be set before selfInit()
cls.populateSeeds(simInstance, modifications)
# we may want to disperse parameters
for disp in simParams.dispersions:
try:
name = disp.getName()
if name not in modifications: # could be using a saved parameter.
modifications[name] = disp.generateString(simInstance)
if simParams.saveDispMag:
magnitudes[name] = disp.generateMagString()
except TypeError:
# This accomodates dispersion variables that are co-dependent
disp.generate()
for i in range(1, disp.numberOfSubDisps+1):
name = disp.getName(i)
if name not in modifications: # could be using a saved parameter.
modifications[name] = disp.generateString(i, simInstance)
if simParams.saveDispMag:
magnitudes[name] = disp.generateMagString()
# if archiving, this run's parameters and random seeds are saved in its own json file
if simParams.shouldArchiveParameters:
# save the dispersions and random seeds for this run
if simParams.icfilename != "":
with open(simParams.icfilename + ".json", 'w') as outfile:
json.dump(modifications, outfile)
else:
with open(simParams.filename + ".json", 'w') as outfile:
json.dump(modifications, outfile)
if simParams.saveDispMag:
with open(simParams.filename + "mag.txt", 'w') as outfileMag:
for k in sorted(magnitudes.keys()):
outfileMag.write("'%s':'%s', \n" % (k, magnitudes[k]))
if simParams.configureFunction is not None:
if simParams.verbose:
print("Configuring sim")
simParams.configureFunction(simInstance)
# apply the dispersions and the random seeds
for variable, value in list(modifications.items()):
disperseStatement = "simInstance." + variable + "=" + value
if simParams.verbose:
print("Executing parameter modification -> ", disperseStatement)
exec(disperseStatement)
# setup data logging
if len(simParams.retentionPolicies) > 0:
if simParams.verbose:
print("Adding retained data")
RetentionPolicy.addRetentionPoliciesToSim(simInstance, simParams.retentionPolicies)
if simParams.verbose:
print("Executing simulation")
# execute the simulation, with the user-supplied executionFunction
try:
simParams.executionFunction(simInstance)
except TypeError:
simParams.executionFunction(simInstance, simParams.filename)
if len(simParams.retentionPolicies) > 0:
if simParams.icfilename != "":
retentionFile = simParams.icfilename + ".data"
else:
retentionFile = simParams.filename + ".data"
if simParams.verbose:
print("Retaining data for run in", retentionFile)
retainedData = RetentionPolicy.getDataForRetention(simInstance, simParams.retentionPolicies)
dataOutQueue.put((retainedData, simParams.index, None))
time.sleep(1)
with gzip.open(retentionFile, "w") as archive:
retainedData["index"] = simParams.index # add run index
pickle.dump(retainedData, archive)
if simParams.verbose:
print("Terminating simulation")
if simParams.verbose:
print("Thread", os.getpid(), "Job", simParams.index, "finished successfully")
return (True, simParams.index) # this function returns true only if the simulation was successful
except Exception as e:
print("Error in worker thread", e)
traceback.print_exc()
return (False, simParams.index) # there was an error
[docs] @staticmethod
def disperseSeeds(simInstance):
"""
Disperses the RNG seeds of all the tasks in the sim, and returns a statement that contains the seeds.
Example return dictionary::
{
'.TaskList[0].TaskModels[1]': 1934586,
'.TaskList[0].TaskModels[2]': 3450093,
'.TaskList[1].TaskModels[0]': 2221934,
'.TaskList[2].TaskModels[0]': 1123244
}
:param simInstance: A basilisk simulation to set random seeds on
:type simInstance: SimulationBaseClass
:return: A dictionary with the random seeds that should be applied to the sim
"""
randomSeeds = {}
for i, task in enumerate(simInstance.TaskList):
for j, model in enumerate(task.TaskModels):
taskVar = 'TaskList[' + str(i) + '].TaskModels' + '[' + str(j) + '].RNGSeed'
rand = str(random.randint(0, 1 << 32 - 1))
try:
execStatement = "simInstance." + taskVar + "=" + str(rand)
exec(execStatement) # if this fails don't add to the list of modification
randomSeeds[taskVar] = rand
except:
pass
return randomSeeds
[docs] @staticmethod
def populateSeeds(simInstance, modifications):
"""
only populate the RNG seeds of all the tasks in the sim
Args:
simInstance: SimulationBaseClass
A basilisk simulation to set random seeds on
modifications:
A dictionary containing RNGSeeds to be populate for the sim, among other sim modifications.
"""
for variable, value in modifications.items():
if ".RNGSeed" in variable:
rngStatement = "simInstance." + variable + "=" + value
exec(rngStatement)