T = FFTN*dt
# Do the computation:
A, lags = autocorr(mu, sigma, tau, R, T, dt, maxS)
################################################################################
# Generating Output:
################################################################################
# Save Results:
if saveResults is True:
pt.pickle({
'mu':mu,
'sigma':sigma,
'tau':tau,
'R':R,
'A':h0,
'lags':Ez,
'dt':dt,
'FFTN':FFTN
}, saveFileName = saveFileName)
# Display results:
if displayResults is True:
print A, lags
# # DDMCube_Slave.py # DDMCubeTeraGrid # # Created by nicain on 4/29/10. # Copyright (c) 2010 __MyCompanyName__. All rights reserved. # ################################################################################ # Preamble ################################################################################ # Import packages: import DDMCube import analysisTools as at import pbsTools as pt # Grab the name of the settings for the run: settings, FD, numberOfJobs, gitVersion = pt.getFromPickleJar(loadDir = './', fileNameSubString = '.settings')[0] # Run the sims: (resultsArray, crossTimesArray) = DDMCube.DDMOU(settings, FD, numberOfJobs[0]) # Save output: pt.pickle((resultsArray, crossTimesArray),saveFileName = 'simResults.dat')
else:
pythonPath = '/usr/lusers/nicain/epd-7.0-2-rh5-x86_64/bin/'
# Beginning computation:
quickName = quickNamePrefix + '-' + quickNameSuffix
numberOfJobs = [simsPerRep, simsPerRep*repsPerProc*procsPerNode*nodes]
# Write a "settings" file:
myUUID = uuid.uuid4()
gitVersion = 'None'
totalLength = 1
for parameter in settings:
thisSetting = settings[parameter]
totalLength *= len(thisSetting)
settingsFileName = join(os.getcwd(), saveResultDir, quickName + '_' + str(myUUID) + '.settings')
pt.pickle((settings, FD, numberOfJobs, gitVersion), saveFileName = settingsFileName)
# Display settings:
at.printSettings(quickName, saveResultDir = saveResultDir)
# Run the job:
tBegin = time.mktime(time.localtime())
pt.runPBS(pythonPath + 'python DDMCube_Slave.py',
fileList = ['DDMCube_Slave.py', settingsFileName, 'DDMCube.so'],
nodes=nodes,
ppn=procsPerNode,
repspp=repsPerProc,
outputDir=outputDir,
runLocation=runLocation,
runType=runType,
waitForSims=waitForSims,
Coh = 6.4
# Initialize
rP = 40 + .4*Coh
rN = 40 - .4*Coh
thetaVals = pl.linspace(thetaMin,thetaMax,thetaN)
# Create the settings dictionary:
settingsDict = {}
for i in range(0,len(thetaVals)):
settingsDict[i+1] = [rP,rN,corr,N,thetaVals[i],dt,nSims,maxY]
# Write out settings file:
settingsFileName = os.path.join(os.getcwd(),'jobSettings.settings')
pt.pickle(settingsDict, saveFileName = settingsFileName)
# Run PBS Job
pt.runPBS(pythonPath + 'python cythonOvershootSlave.py $ID',
fileList = ['cythonOvershootSlave.py', settingsFileName, 'cythonOvershootSIP.so'],
nodes=nodes,
ppn=procsPerNode,
repspp=repsPerProc,
outputDir=outputDir,
runLocation=runLocation,
runType=runType,
waitForSims=waitForSims,
wallTime=wallTime,
dryRun=dryRun,
queue=queue,
wallTimeEstCount=wallTimeEstCount)
import pylab as pl import sys import time # Grab the settings from the file: settingsDict = pt.getFromPickleJar(loadDir = './', fileNameSubString = '.settings')[0] # Grab the index for the current job: thetaInd = int(sys.argv[1]) # Write down the settings for the job: rP, rN, corr, N, theta, dt, nSims, maxY = settingsDict[thetaInd] # Run the sim, recovering monte carlo data: tic = time.time() overShootTemp = getOvershootDist(rP, rN, corr, N, theta-.01, dt, nSims) print time.time()-tic, 'sec Elapsed' # Make the histogram: bins = range(0,maxY) overShootPref = overShootTemp[overShootTemp>0]-theta mean = overShootPref.mean() overShootHist, bin_edges = pl.histogram(overShootPref,bins=bins) overShootHist = overShootHist*1.0/overShootHist.sum() # Save output: pt.pickle((overShootHist,mean),saveFileName = 'simResults_' + str(theta) + '.dat')
