def __init__(self):
self.params = initsim.getparams()
if len(self.params['numwindows']) == 1:
self.windowcentres = [[self.params['firstwindow'][0] + n*self.params['windowsep'][0]] \
for n in range(self.params['numwindows'][0])]
elif len(self.params['numwindows']) == 2:
self.windowcentres = [[self.params['firstwindow'][0] + n*self.params['windowsep'][0], \
self.params['firstwindow'][1] + m*self.params['windowsep'][1]] \
for m in range(self.params['numwindows'][1]) \
for n in range(self.params['numwindows'][0])]
else:
print " > 2 order parameters not supported"
exit(0)
self.dir = os.path.abspath(__file__)
funcman = funcselector.FuncSelector(self.params)
self.writexyz = funcman.WriteXyzFunc()
self.params['simulation'] = 'restart'
# if queue_name_form supplied, the queue name will be this with the
# window index appended
self.queue_name_form = 'unnamed'
# pickled version of master (wumbrella) parameters 'params.pkl'
writeoutput.writepickparams(self.params)
# human readable version 'params.out'
writeoutput.writeparams(self.params)
def __init__(self):
"""
Read parameters, get initial positions (and velocities if MD),
and setup any parameters needed for the simulation.
"""
# read input parameters and write to file
self.params = initsim.getparams()
# pickled version 'params.pkl'
writeoutput.writepickparams(self.params)
# human readable version 'params.out'
writeoutput.writeparams(self.params)
# From params dictionary create FuncSelector object. This
# will handle correct selection of the underlying fortran/C++
# functions correctly (the functions called depend on the
# potential, i.e. the value of params['potential'], and also
# on the type of MC cycle wanted, i.e. params['mctype'], and
# on the order parameter desired, params['orderparam'].
funcman = funcselector.FuncSelector(self.params)
self.totalenergy = funcman.TotalEnergyFunc()
self.runcycle = funcman.MCCycleFunc()
self.orderp = funcman.OrderParamFunc()
self.writexyz = funcman.WriteXyzFunc()
# initialize positions (and velocities and forces if we are
# doing MD rather than MC).
if self.params['mctype'] == 'md':
self.positions, \
self.velocities = initsim.\
initpositionsvelocities(self.params)
# Note we only have MD implemented for Gaussian potential
# at present; the force function should in principle be
# handled by the FuncSelector interface.
self.forces = force.gauss_forceslist(self.positions,
self.params)
else:
# MC simulation. We initialize positions only.
self.positions = initsim.initpositions(self.params)
# write initial positions to file if new simulation
if self.params['simulation'] == 'new':
self.writexyz('initpositions.xyz', self.positions,
self.params)
# write initial pickle file that stores both positions and
# velocities if we are doing an MD simulation.
if self.params['mctype'] == 'md':
writeoutput.writemdpick('initpositions.pkl',
self.positions, self.velocities)
# number of times to call MC cycle function
self.ncall = int(np.ceil(self.params['ncycle'] /
float(self.params['opsamp'])))
# number of cycles each time we call MC cycle function
self.params['cycle'] = min(self.params['ncycle'],
self.params['opsamp'])
def __init__(self):
"""
Read parameters, get initial positions,
and setup any parameters needed for the simulation.
"""
# allow taking index as command line argument
if len(sys.argv) != 1 and sys.argv[1] == '-w':
self.iwind = sys.argv[2]
else:
self.iwind = ''
# read input parameters and write to file
if self.iwind != '' and os.path.exists('params{0}.pkl'.format(
self.iwind)):
self.params = pickle.load(open('params{0}.pkl'.format(self.iwind)))
else:
self.params = initsim.getparams()
if self.iwind == '': #indexed pkl files wil already exist otherwise
# pickled version 'params.pkl'
writeoutput.writepickparams(self.params)
# human readable version 'params.out'
writeoutput.writeparams(self.params)
# allow some equilibration cycles
if self.params['umbequilcycles'] > 0:
self.params['umbequil'] = True
else:
self.params['umbequil'] = False
# From params dictionary create FuncSelector object. This
# will handle correct selection of the underlying fortran/C++
# functions correctly (the functions called depend on the
# potential, i.e. the value of params['potential'], and also
# on the type of MC cycle wanted, i.e. params['mctype'], and
# on the order parameter desired, params['orderparam'].
funcman = funcselector.FuncSelector(self.params)
self.totalenergy = funcman.TotalEnergyFunc()
self.runcycle = funcman.MCCycleFunc()
self.orderp = funcman.OrderParamFunc()
self.writexyz = funcman.WriteXyzFunc()
# initialize positions
self.positions = initsim.initpositions(self.params)
# write initial positions to file if new simulation
if self.params['simulation'] == 'new':
self.writexyz('initpositions{0}.xyz'.format(self.iwind),
self.positions, self.params)
# number of times to call MC cycle function
self.numbrellacycles = self.params['numbrellacycles']
# number of cycles each time we call MC cycle function
self.params['cycle'] = self.params['nunbiased']
# full path is the complete path to codeffs.py
fullpath = os.path.abspath(os.path.dirname(sys.argv[0]))
# epath is path to executables lambda0.py, finish.py, takeshot.py
epath = os.path.normpath(os.path.join(fullpath, '..', 'scripts'))
print 'path to scripts lambda0.py, finish.py, takeshot.py is', epath
# can specify a job queue here
qname = 'physics.q'
if qname:
qstr = '-q %s' % qname
else:
qstr = ''
# get params and write to pickle file 'params.pkl' for future reading
params = initsim.getparams()
writeoutput.writepickparams(params)
# write params to 'pickle.out' -> human readable version of params.pkl
writeoutput.writeparams(params)
# params needed for job submission
numint = params['numint']
nshots = params['nshots']
nbatch = params['nbatch']
minsuccess = params['minsuccess']
ffsnm = params['ffsname']
ffsre = params['ffsrestart']
# syntactic sugar - if ffsre = FFSNEW, we are starting new simulation
FFSNEW = -1