def main(fileprefix, KijFilePrefix=None):
if KijFilePrefix is None:
KijFilePrefix = fileprefix
# fileprefix='140415a'
# Datafolder
dataFolder = 'DATA'
outputFolder = dataFolder + '/' + fileprefix + '_tar'
# Post-processor searches/saves file "KijFilePrefix.Kij.npy"
# KijFilePrefix='130415a'
# KijFilePrefix=fileprefix
# Use this restart file if the force constant file cannot be found
restartFile = KijFilePrefix + '.quenched.restart'
# Create the data folder
from subprocess import call
command = ["mkdir", "-p", outputFolder]
print " ".join(command)
call(command)
dt_md = 2.5e-15 # Timestep used in MD, affects the frequency grid
widthWin = 0.1e12 # Width of the Daniell smoothing window in Hz
# The velocity dump file from LAMMPS
fileVels = fileprefix + '.vels.dat'
# The compactly formatted velocity file, produced using a C++ script if not found
fileCompactVels = fileprefix + '.vels.dat.compact'
# Correct the units, this assumes the unit of eV/(A^2)*(A/ps)^2 for the v_iK_{ij}v_j product (LAMMPS metal units)
scaleFactor = 1.602e-19 / (1e-20) * 1e4
# Prepare the post-processor
pP = SHCPostProc(fileCompactVels,
KijFilePrefix,
dt_md=dt_md,
scaleFactor=scaleFactor,
LAMMPSDumpFile=fileVels,
widthWin=widthWin,
LAMMPSRestartFile=restartFile,
NChunks=100,
chunkSize=50000,
backupPrefix=fileprefix,
reCalcVels=False,
reCalcFC=False)
# Post-process
pP.postProcess() # All variables will be contained in the object pP
# Various output options
# Pickling the post-processing instance
import cPickle as pickle
with open(outputFolder + '/' + fileprefix + '_PP.pckl', 'w') as f:
pickle.dump(pP, f)
# Saving into numpy files
np.save(outputFolder + '/' + fileprefix + '_oms.npy', pP.oms_fft)
np.save(outputFolder + '/' + fileprefix + '_SHC.npy', pP.SHC_smooth)
# Saving to file
print "Saving to file " + outputFolder + '/' + fileprefix + '_SHC.txt'
np.savetxt(outputFolder + '/' + fileprefix + '_SHC.txt',
np.column_stack((pP.oms_fft, pP.SHC_smooth)))
# Copy relevant files to the DATA folder
command = "cp " + fileprefix + '.aveinput_*.dat' + " " + outputFolder
print command
call(command, shell=True)
command = "cp " + fileprefix + '.Ti*.dat' + " " + outputFolder
print command
call(command, shell=True)
# Tar the output folder without the DATA folder
command = [
"tar", "-czvf", fileprefix + '_tar.tgz', '--directory=' + dataFolder,
outputFolder.strip(dataFolder + '/')
]
print " ".join(command)
call(command)
def main(fileprefix,KijFilePrefix=None):
if KijFilePrefix is None:
KijFilePrefix=fileprefix
# fileprefix='140415a'
# Datafolder
dataFolder='DATA'
outputFolder=dataFolder+'/'+fileprefix+'_tar'
# Post-processor searches/saves file "KijFilePrefix.Kij.npy"
# KijFilePrefix='130415a'
# KijFilePrefix=fileprefix
# Use this restart file if the force constant file cannot be found
restartFile=KijFilePrefix+'.quenched.restart'
# Create the data folder
from subprocess import call
command=["mkdir","-p",outputFolder]
print " ".join(command)
call(command)
dt_md=2.5e-15 # Timestep used in MD, affects the frequency grid
widthWin=0.1e12 # Width of the Daniell smoothing window in Hz
# The velocity dump file from LAMMPS
fileVels=fileprefix+'.vels.dat'
# The compactly formatted velocity file, produced using a C++ script if not found
fileCompactVels=fileprefix+'.vels.dat.compact'
# Correct the units, this assumes the unit of eV/(A^2)*(A/ps)^2 for the v_iK_{ij}v_j product (LAMMPS metal units)
scaleFactor=1.602e-19/(1e-20)*1e4
# Prepare the post-processor
pP=SHCPostProc(fileCompactVels,KijFilePrefix,
dt_md=dt_md,scaleFactor=scaleFactor,
LAMMPSDumpFile=fileVels,widthWin=widthWin,
LAMMPSRestartFile=restartFile,
NChunks=100,chunkSize=50000,
backupPrefix=fileprefix,
reCalcVels=False,
reCalcFC=False)
# Post-process
pP.postProcess() # All variables will be contained in the object pP
# Various output options
# Pickling the post-processing instance
import cPickle as pickle
with open(outputFolder+'/'+fileprefix+'_PP.pckl','w') as f:
pickle.dump(pP,f)
# Saving into numpy files
np.save(outputFolder+'/'+fileprefix+'_oms.npy',pP.oms_fft)
np.save(outputFolder+'/'+fileprefix+'_SHC.npy',pP.SHC_smooth)
# Saving to file
print "Saving to file "+outputFolder+'/'+fileprefix+'_SHC.txt'
np.savetxt(outputFolder+'/'+fileprefix+'_SHC.txt',np.column_stack((pP.oms_fft,pP.SHC_smooth)))
# Copy relevant files to the DATA folder
command="cp "+fileprefix+'.aveinput_*.dat'+" "+outputFolder
print command
call(command,shell=True)
command="cp "+fileprefix+'.Ti*.dat'+" "+outputFolder
print command
call(command,shell=True)
# Tar the output folder without the DATA folder
command=["tar","-czvf",fileprefix+'_tar.tgz','--directory='+dataFolder,outputFolder.strip(dataFolder+'/')]
print " ".join(command)
call(command)
# Prepare the post-processor
pP = SHCPostProc(fileCompactVels,
KijFilePrefix,
dt_md=dt_md,
scaleFactor=scaleFactor,
LAMMPSDumpFile=fileVels,
widthWin=widthWin,
LAMMPSRestartFile='270115a.quenched.restart',
NChunks=200,
chunkSize=50000,
backupPrefix=fileprefix,
reCalcVels=False,
reCalcFC=False)
# Post-process
pP.postProcess() # All variables will be contained in the object pP
# Various output options
# Pickling the post-processing instance
import cPickle as pickle
with open(outputFolder + '/' + fileprefix + '_PP.pckl', 'w') as f:
pickle.dump(pP, f)
# Saving into numpy files
np.save(outputFolder + '/' + fileprefix + '_oms.npy', pP.oms_fft)
np.save(outputFolder + '/' + fileprefix + '_SHC.npy', pP.SHC_smooth)
# Saving to file
print "Saving to file " + outputFolder + '/' + fileprefix + '_SHC.txt'
np.savetxt(outputFolder + '/' + fileprefix + '_SHC.txt',
# Use this restart file if the force constant file cannot be found
restartFile=KijFilePrefix+'.quenched.restart'
# Correct the units, this assumes the unit of eV/(A^2)*(A/ps)^2 for the v_iK_{ij}v_j product (LAMMPS metal units)
scaleFactor=1.602e-19/(1e-20)*1e4
# Prepare the post-processor
pP=SHCPostProc(fileCompactVels,KijFilePrefix,
dt_md=dt_md,scaleFactor=scaleFactor,
LAMMPSDumpFile=fileVels,widthWin=widthWin,
LAMMPSRestartFile='270115a.quenched.restart',
NChunks=200,chunkSize=50000,
backupPrefix=fileprefix,
reCalcVels=False,
reCalcFC=False)
# Post-process
pP.postProcess() # All variables will be contained in the object pP
# Various output options
# Pickling the post-processing instance
import cPickle as pickle
with open(outputFolder+'/'+fileprefix+'_PP.pckl','w') as f:
pickle.dump(pP,f)
# Saving into numpy files
np.save(outputFolder+'/'+fileprefix+'_oms.npy',pP.oms_fft)
np.save(outputFolder+'/'+fileprefix+'_SHC.npy',pP.SHC_smooth)
# Saving to file
print "Saving to file "+outputFolder+'/'+fileprefix+'_SHC.txt'
np.savetxt(outputFolder+'/'+fileprefix+'_SHC.txt',np.column_stack((pP.oms_fft,pP.SHC_smooth)))
