def submit_jobs(self, mvals, AGrad=False, AHess=False):
n=0
id_string = "%s_%i-%i" % (self.name, Counter(), n)
while os.path.exists('%s.out' % id_string):
n+=1
id_string = "%s_%i-%i" % (self.name, Counter(), n)
with open('forcebalance.p','w') as f: forcebalance.nifty.lp_dump((mvals, AGrad, AHess, id_string, self.r_options, self.r_tgt_opts, self.FF),f)
forcebalance.nifty.LinkFile(os.path.join(os.path.split(__file__)[0],"data","rtarget.py"),"rtarget.py")
forcebalance.nifty.LinkFile(os.path.join(self.root,"temp", self.name, "target.tar.bz2"),"%s.tar.bz2" % self.name)
wq = getWorkQueue()
logger.info("Sending target '%s' to work queue for remote evaluation\n" % self.name)
# input:
# forcebalance.p: pickled mvals, options, and forcefield
# rtarget.py: remote target evaluation script
# target.tar.bz2: tarred target
# output:
# objective.p: pickled objective function dictionary
# indicate.log: results of target.indicate() written to file
forcebalance.nifty.queue_up(wq, "python rtarget.py > %s.out 2>&1" % id_string,
["forcebalance.p", "rtarget.py", "%s.tar.bz2" % self.name],
['objective_%s.p' % id_string, 'indicate_%s.log' % id_string, '%s.out' % id_string],
tgt=self)
self.id_string = id_string
def Target_Terms(self, mvals, Order=0, verbose=False, customdir=None):
## This is the objective function; it's a dictionary containing the value, first and second derivatives
Objective = {'X':0.0, 'G':np.zeros(self.FF.np), 'H':np.zeros((self.FF.np,self.FF.np))}
# Loop through the targets, stage the directories and submit the Work Queue processes.
for Tgt in self.Targets:
Tgt.stage(mvals, AGrad = Order >= 1, AHess = Order >= 2, customdir=customdir)
if self.asynchronous:
# Asynchronous evaluation of objective function and Work Queue tasks.
# Create a list of the targets, and remove them from the list as they are finished.
Need2Evaluate = self.Targets[:]
# This ensures that the OrderedDict doesn't get out of order.
for Tgt in self.Targets:
self.ObjDict[Tgt.name] = None
# Loop through the targets and compute the objective function for ones that are finished.
while len(Need2Evaluate) > 0:
for Tgt in Need2Evaluate:
if Tgt.wq_complete():
# List of functions that I can call.
Funcs = [Tgt.get_X, Tgt.get_G, Tgt.get_H]
# Call the appropriate function
Ans = Funcs[Order](mvals, customdir=customdir)
# Print out the qualitative indicators
if verbose:
Tgt.meta_indicate(customdir=customdir)
# Note that no matter which order of function we call, we still increment the objective / gradient / Hessian the same way.
if not in_fd():
self.ObjDict[Tgt.name] = {'w' : Tgt.weight/self.WTot , 'x' : Ans['X']}
for i in range(3):
Objective[Letters[i]] += Ans[Letters[i]]*Tgt.weight/self.WTot
Need2Evaluate.remove(Tgt)
break
else:
pass
else:
wq = getWorkQueue()
if wq != None:
wq_wait(wq)
for Tgt in self.Targets:
# The first call is always done at the midpoint.
Tgt.bSave = True
# List of functions that I can call.
Funcs = [Tgt.get_X, Tgt.get_G, Tgt.get_H]
# Call the appropriate function
Ans = Funcs[Order](mvals, customdir=customdir)
# Print out the qualitative indicators
if verbose:
Tgt.meta_indicate(customdir=customdir)
# Note that no matter which order of function we call, we still increment the objective / gradient / Hessian the same way.
if not in_fd():
self.ObjDict[Tgt.name] = {'w' : Tgt.weight/self.WTot , 'x' : Ans['X']}
for i in range(3):
Objective[Letters[i]] += Ans[Letters[i]]*Tgt.weight/self.WTot
# The target has evaluated at least once.
for Tgt in self.Targets:
Tgt.evaluated = True
# Safeguard to make sure we don't have exact zeros on Hessian diagonal
for i in range(self.FF.np):
if Objective['H'][i,i] == 0.0:
Objective['H'][i,i] = 1.0
return Objective
def submit_jobs(self, mvals, AGrad=False, AHess=False):
id_string = "%s_iter%04i" % (self.name, Counter())
self.serialize_ff(mvals, outside="forcefield-remote")
forcebalance.nifty.lp_dump((AGrad, AHess, id_string, self.r_options, self.r_tgt_opts, self.pgrad),'options.p')
# Link in the rpfx script.
if len(self.rpfx) > 0:
forcebalance.nifty.LinkFile(os.path.join(os.path.split(__file__)[0],"data",self.rpfx),self.rpfx)
forcebalance.nifty.LinkFile(os.path.join(os.path.split(__file__)[0],"data","rtarget.py"),"rtarget.py")
forcebalance.nifty.LinkFile(os.path.join(self.root, self.tempdir, "target.tar.bz2"),"target.tar.bz2")
wq = getWorkQueue()
# logger.info("Sending target '%s' to work queue for remote evaluation\n" % self.name)
# input:
# forcefield.p: pickled force field
# options.p: pickled mvals, options
# rtarget.py: remote target evaluation script
# target.tar.bz2: tarred target
# output:
# objective.p: pickled objective function dictionary
# indicate.log: results of target.indicate() written to file
# if len(self.rpfx) > 0 and self.rpfx not in ['rungmx.sh', 'runcuda.sh']:
# logger.error('Unsupported prefix script for launching remote target')
# raise RuntimeError
forcebalance.nifty.queue_up(wq, "%spython rtarget.py > rtarget.out 2>&1" % (("sh %s%s " % (self.rpfx, " -b" if self.rbak else ""))
if len(self.rpfx) > 0 else ""),
["forcefield.p", "options.p", "rtarget.py", "target.tar.bz2"] + ([self.rpfx] if len(self.rpfx) > 0 else []),
['objective.p', 'indicate.log', 'rtarget.out'],
tgt=self, tag=self.name, verbose=False)
def npt_simulation(self, temperature, pressure, simnum):
""" Submit a NPT simulation to the Work Queue. """
wq = getWorkQueue()
if not (os.path.exists('npt_result.p') or os.path.exists('npt_result.p.bz2')):
link_dir_contents(os.path.join(self.root,self.rundir),os.getcwd())
if wq == None:
print "Running condensed phase simulation locally."
print "You may tail -f %s/npt.out in another terminal window" % os.getcwd()
# if GoodStep() and (temperature, pressure) in self.DynDict_New:
# self.DynDict[(temperature, pressure)] = self.DynDict_New[(temperature, pressure)]
# if (temperature, pressure) in self.DynDict:
# dynsrc = self.DynDict[(temperature, pressure)]
# dyndest = os.path.join(os.getcwd(), 'liquid.dyn')
# print "Copying .dyn file: %s to %s" % (dynsrc, dyndest)
# shutil.copy2(dynsrc,dyndest)
cmdstr = 'python npt.py gromacs %i %.3f %.3f %.3f %.3f %s --liquid_equ_steps %i &> npt.out' % \
(self.liquid_prod_steps, self.liquid_timestep, self.liquid_interval, temperature, pressure, " --minimize_energy" if self.minimize_energy else "", self.liquid_equ_steps)
_exec(cmdstr)
# self.DynDict_New[(temperature, pressure)] = os.path.join(os.getcwd(),'liquid.dyn')
else:
# This part of the code has never been used before
# Still need to figure out where to specify GROMACS location on each cluster
# queue_up(wq,
# command = 'python npt.py liquid.xyz %.3f %.3f &> npt.out' % (temperature, pressure),
# input_files = ['liquid.xyz','liquid.key','mono.xyz','mono.key','forcebalance.p','npt.py'],
# output_files = ['npt_result.p.bz2', 'npt.py'] + self.FF.fnms,
# tgt=self)
raise RuntimeError('Remote GROMACS execution is not yet enabled')
def run_simulation(self, label, liq, AGrad=True):
"""
Submit a simulation to the Work Queue or run it locally.
Inputs:
label = The name of the molecule (and hopefully the folder name that you're running in)
liq = True/false flag indicating whether to run in liquid or gas phase
"""
wq = getWorkQueue()
# Create a dictionary of MD options that the script will read.
md_opts = OrderedDict()
md_opts['temperature'] = self.hfe_temperature
md_opts['pressure'] = self.hfe_pressure
md_opts['minimize'] = True
if liq:
sdnm = 'liq'
md_opts['nequil'] = self.liquid_eq_steps
md_opts['nsteps'] = self.liquid_md_steps
md_opts['timestep'] = self.liquid_timestep
md_opts['sample'] = self.liquid_interval
else:
sdnm = 'gas'
md_opts['nequil'] = self.gas_eq_steps
md_opts['nsteps'] = self.gas_md_steps
md_opts['timestep'] = self.gas_timestep
md_opts['sample'] = self.gas_interval
eng_opts = deepcopy(self.engine_opts)
# Enforce implicit solvent in the liquid simulation.
# We need to be more careful with this when running explicit solvent.
eng_opts['implicit_solvent'] = liq
eng_opts['coords'] = os.path.basename(self.molecules[label])
if not os.path.exists(sdnm):
os.makedirs(sdnm)
os.chdir(sdnm)
if not os.path.exists('md_result.p'):
# Link in a bunch of files... what were these again?
link_dir_contents(os.path.join(self.root,self.rundir),os.getcwd())
# Link in the scripts required to run the simulation
for f in self.scripts:
LinkFile(os.path.join(os.path.split(__file__)[0],"data",f),os.path.join(os.getcwd(),f))
# Link in the coordinate file.
LinkFile(self.molecules[label], './%s' % os.path.basename(self.molecules[label]))
# Store names of previous trajectory files.
self.last_traj += [os.path.join(os.getcwd(), i) for i in self.extra_output]
# Write target, engine and simulation options to disk.
lp_dump((self.OptionDict, eng_opts, md_opts), 'simulation.p')
# Execute the script for running molecular dynamics.
cmdstr = '%s python md_ism_hfe.py %s' % (self.prefix, "-g" if AGrad else "")
if wq == None:
logger.info("Running condensed phase simulation locally.\n")
logger.info("You may tail -f %s/npt.out in another terminal window\n" % os.getcwd())
_exec(cmdstr, copy_stderr=True, outfnm='md.out')
else:
queue_up(wq, command = cmdstr+' &> md.out', tag='%s:%s/%s' % (self.name, label, "liq" if liq else "gas"),
input_files = self.scripts + ['simulation.p', 'forcefield.p', os.path.basename(self.molecules[label])],
output_files = ['md_result.p', 'md.out'] + self.extra_output, tgt=self, verbose=False, print_time=3600)
os.chdir('..')
def Target_Terms(self, mvals, Order=0, verbose=False, customdir=None):
## This is the objective function; it's a dictionary containing the value, first and second derivatives
Objective = {'X':0.0, 'G':np.zeros(self.FF.np), 'H':np.zeros((self.FF.np,self.FF.np))}
# Loop through the targets, stage the directories and submit the Work Queue processes.
for Tgt in self.Targets:
Tgt.stage(mvals, AGrad = Order >= 1, AHess = Order >= 2, customdir=customdir)
if self.asynchronous:
# Asynchronous evaluation of objective function and Work Queue tasks.
# Create a list of the targets, and remove them from the list as they are finished.
Need2Evaluate = self.Targets[:]
# This ensures that the OrderedDict doesn't get out of order.
for Tgt in self.Targets:
self.ObjDict[Tgt.name] = None
# Loop through the targets and compute the objective function for ones that are finished.
while len(Need2Evaluate) > 0:
for Tgt in Need2Evaluate:
if Tgt.wq_complete():
# List of functions that I can call.
Funcs = [Tgt.get_X, Tgt.get_G, Tgt.get_H]
# Call the appropriate function
Ans = Funcs[Order](mvals, customdir=customdir)
# Print out the qualitative indicators
if verbose:
Tgt.meta_indicate(customdir=customdir)
# Note that no matter which order of function we call, we still increment the objective / gradient / Hessian the same way.
if not in_fd():
self.ObjDict[Tgt.name] = {'w' : Tgt.weight/self.WTot , 'x' : Ans['X']}
for i in range(3):
Objective[Letters[i]] += Ans[Letters[i]]*Tgt.weight/self.WTot
Need2Evaluate.remove(Tgt)
break
else:
pass
else:
wq = getWorkQueue()
if wq is not None:
wq_wait(wq)
for Tgt in self.Targets:
# The first call is always done at the midpoint.
Tgt.bSave = True
# List of functions that I can call.
Funcs = [Tgt.get_X, Tgt.get_G, Tgt.get_H]
# Call the appropriate function
Ans = Funcs[Order](mvals, customdir=customdir)
# Print out the qualitative indicators
if verbose:
Tgt.meta_indicate(customdir=customdir)
# Note that no matter which order of function we call, we still increment the objective / gradient / Hessian the same way.
if not in_fd():
self.ObjDict[Tgt.name] = {'w' : Tgt.weight/self.WTot , 'x' : Ans['X']}
for i in range(3):
Objective[Letters[i]] += Ans[Letters[i]]*Tgt.weight/self.WTot
# The target has evaluated at least once.
for Tgt in self.Targets:
Tgt.evaluated = True
# Safeguard to make sure we don't have exact zeros on Hessian diagonal
for i in range(self.FF.np):
if Objective['H'][i,i] == 0.0:
Objective['H'][i,i] = 1.0
return Objective
def submit_jobs(self, mvals, AGrad=False, AHess=False):
id_string = "%s_iter%04i" % (self.name, Counter())
self.serialize_ff(mvals, outside="forcefield-remote")
forcebalance.nifty.lp_dump((AGrad, AHess, id_string, self.r_options, self.r_tgt_opts, self.pgrad),'options.p')
# Link in the rpfx script.
if len(self.rpfx) > 0:
forcebalance.nifty.LinkFile(os.path.join(os.path.split(__file__)[0],"data",self.rpfx),self.rpfx)
forcebalance.nifty.LinkFile(os.path.join(os.path.split(__file__)[0],"data","rtarget.py"),"rtarget.py")
forcebalance.nifty.LinkFile(os.path.join(self.root, self.tempdir, "target.tar.bz2"),"target.tar.bz2")
wq = getWorkQueue()
# logger.info("Sending target '%s' to work queue for remote evaluation\n" % self.name)
# input:
# forcefield.p: pickled force field
# options.p: pickled mvals, options
# rtarget.py: remote target evaluation script
# target.tar.bz2: tarred target
# output:
# objective.p: pickled objective function dictionary
# indicate.log: results of target.indicate() written to file
# if len(self.rpfx) > 0 and self.rpfx not in ['rungmx.sh', 'runcuda.sh']:
# logger.error('Unsupported prefix script for launching remote target')
# raise RuntimeError
forcebalance.nifty.queue_up(wq, "%spython rtarget.py > rtarget.out 2>&1" % (("sh %s%s " % (self.rpfx, " -b" if self.rbak else ""))
if len(self.rpfx) > 0 else ""),
["forcefield.p", "options.p", "rtarget.py", "target.tar.bz2"] + ([self.rpfx] if len(self.rpfx) > 0 else []),
['objective.p', 'indicate.log', 'rtarget.out'],
tgt=self, tag=self.name, verbose=False)
def run_simulation(self, label, liq, AGrad=True):
"""
Submit a simulation to the Work Queue or run it locally.
Inputs:
label = The name of the molecule (and hopefully the folder name that you're running in)
liq = True/false flag indicating whether to run in liquid or gas phase
"""
wq = getWorkQueue()
# Create a dictionary of MD options that the script will read.
md_opts = OrderedDict()
md_opts['temperature'] = self.hfe_temperature
md_opts['pressure'] = self.hfe_pressure
md_opts['minimize'] = True
if liq:
sdnm = 'liq'
md_opts['nequil'] = self.liquid_eq_steps
md_opts['nsteps'] = self.liquid_md_steps
md_opts['timestep'] = self.liquid_timestep
md_opts['sample'] = self.liquid_interval
else:
sdnm = 'gas'
md_opts['nequil'] = self.gas_eq_steps
md_opts['nsteps'] = self.gas_md_steps
md_opts['timestep'] = self.gas_timestep
md_opts['sample'] = self.gas_interval
eng_opts = deepcopy(self.engine_opts)
# Enforce implicit solvent in the liquid simulation.
# We need to be more careful with this when running explicit solvent.
eng_opts['implicit_solvent'] = liq
eng_opts['coords'] = os.path.basename(self.molecules[label])
if not os.path.exists(sdnm):
os.makedirs(sdnm)
os.chdir(sdnm)
if not os.path.exists('md_result.p'):
# Link in a bunch of files... what were these again?
link_dir_contents(os.path.join(self.root,self.rundir),os.getcwd())
# Link in the scripts required to run the simulation
for f in self.scripts:
LinkFile(os.path.join(os.path.split(__file__)[0],"data",f),os.path.join(os.getcwd(),f))
# Link in the coordinate file.
LinkFile(self.molecules[label], './%s' % os.path.basename(self.molecules[label]))
# Store names of previous trajectory files.
self.last_traj += [os.path.join(os.getcwd(), i) for i in self.extra_output]
# Write target, engine and simulation options to disk.
lp_dump((self.OptionDict, eng_opts, md_opts), 'simulation.p')
# Execute the script for running molecular dynamics.
cmdstr = '%s python md_ism_hfe.py %s' % (self.prefix, "-g" if AGrad else "")
if wq is None:
logger.info("Running condensed phase simulation locally.\n")
logger.info("You may tail -f %s/npt.out in another terminal window\n" % os.getcwd())
_exec(cmdstr, copy_stderr=True, outfnm='md.out')
else:
queue_up(wq, command = cmdstr+' &> md.out', tag='%s:%s/%s' % (self.name, label, "liq" if liq else "gas"),
input_files = self.scripts + ['simulation.p', 'forcefield.p', os.path.basename(self.molecules[label])],
output_files = ['md_result.p', 'md.out'] + self.extra_output, tgt=self, verbose=False, print_time=3600)
os.chdir('..')
def wq_complete(self):
""" This method determines whether the Work Queue tasks for the current target have completed. """
wq = getWorkQueue()
WQIds = getWQIds()
if wq is None:
return True
elif wq.empty():
WQIds[self.name] = []
return True
elif len(WQIds[self.name]) == 0:
return True
else:
wq_wait1(wq, wait_time=30)
if len(WQIds[self.name]) == 0:
return True
else:
return False
def wq_complete(self):
""" This method determines whether the Work Queue tasks for the current target have completed. """
wq = getWorkQueue()
WQIds = getWQIds()
if wq == None:
return True
elif wq.empty():
WQIds[self.name] = []
return True
elif len(WQIds[self.name]) == 0:
return True
else:
wq_wait1(wq, wait_time=30)
if len(WQIds[self.name]) == 0:
return True
else:
return False
def interaction_driver_all(self, dielectric=False):
""" Computes the energy and force using GROMACS for a trajectory. This does not require GROMACS-X2. """
# Remove backup files.
rm_gmx_baks(os.getcwd())
# Do the interacting calculation.
_exec(["./grompp", "-f", "interaction.mdp", "-n", "index.ndx"], print_command=False)
_exec(["./mdrun", "-nt", "1", "-rerunvsite", "-rerun", "all.gro"], print_command=False)
# Gather information
_exec(["./g_energy","-xvg","no"], print_command=False, stdin="Potential\n")
Interact = array([float(l.split()[1]) for l in open('energy.xvg').readlines()])
# Do the excluded calculation.
_exec(["./grompp", "-f", "excluded.mdp", "-n", "index.ndx"], print_command=False)
_exec(["./mdrun", "-nt", "1", "-rerunvsite", "-rerun", "all.gro"], print_command=False)
# Gather information
_exec(["./g_energy","-xvg","no"], print_command=False, stdin="Potential\n")
Excluded = array([float(l.split()[1]) for l in open('energy.xvg').readlines()])
# The interaction energy.
M = Interact - Excluded
# Now we have the MM interaction energy.
# We need the COSMO component of the interaction energy now...
if dielectric:
traj_dimer = deepcopy(self.traj)
traj_dimer.add_quantum("qtemp_D.in")
traj_dimer.write("qchem_dimer.in",ftype="qcin")
traj_monoA = deepcopy(self.traj)
traj_monoA.add_quantum("qtemp_A.in")
traj_monoA.write("qchem_monoA.in",ftype="qcin")
traj_monoB = deepcopy(self.traj)
traj_monoB.add_quantum("qtemp_B.in")
traj_monoB.write("qchem_monoB.in",ftype="qcin")
wq = getWorkQueue()
if wq == None:
warn_press_key("To proceed past this point, a Work Queue must be present")
print "Computing the dielectric energy"
Diel_D = QChem_Dielectric_Energy("qchem_dimer.in",wq)
Diel_A = QChem_Dielectric_Energy("qchem_monoA.in",wq)
# The dielectric energy for a water molecule should never change.
if hasattr(self,"Diel_B"):
Diel_B = self.Diel_B
else:
Diel_B = QChem_Dielectric_Energy("qchem_monoB.in",self.wq)
self.Diel_B = Diel_B
self.Dielectric = Diel_D - Diel_A - Diel_B
M += self.Dielectric
return M
def calc_wq_new(self, coords, dirname):
wq = getWorkQueue()
if not os.path.exists(dirname): os.makedirs(dirname)
# Convert coordinates back to the xyz file<
self.M.xyzs[0] = coords.reshape(-1, 3) * bohr2ang
self.M.edit_qcrems({'jobtype': 'force'})
self.M[0].write(os.path.join(dirname, 'run.in'))
in_files = [('%s/run.in' % dirname, 'run.in')]
out_files = [('%s/run.out' % dirname, 'run.out'),
('%s/run.log' % dirname, 'run.log')]
if self.qcdir:
raise RuntimeError(
"--qcdir currently not supported with Work Queue")
queue_up_src_dest(wq,
"qchem%s run.in run.out &> run.log" % self.nt(),
in_files,
out_files,
verbose=False)
def get(self, mvals, AGrad=False, AHess=False):
"""
LPW 04-17-2013
This subroutine builds the objective function from Psi4.
@param[in] mvals Mathematical parameter values
@param[in] AGrad Switch to turn on analytic gradient
@param[in] AHess Switch to turn on analytic Hessian
@return Answer Contribution to the objective function
"""
Answer = {}
Fac = 1000000
n = len(mvals)
X = 0.0
G = np.zeros(n)
H = np.zeros((n,n))
pvals = self.FF.make(mvals)
self.tdir = os.getcwd()
self.objd = OrderedDict()
self.gradd = OrderedDict()
self.hdiagd = OrderedDict()
wq = getWorkQueue()
def fdwrap2(func,mvals0,pidx,qidx,key=None,**kwargs):
def func2(arg1,arg2):
mvals = list(mvals0)
mvals[pidx] += arg1
mvals[qidx] += arg2
logger.info("\rfdwrap2:" + func.__name__ + "[%i] = % .1e , [%i] = % .1e" % (pidx, arg1, qidx, arg2) + ' '*50)
if key != None:
return func(mvals,**kwargs)[key]
else:
return func(mvals,**kwargs)
return func2
def f2d5p(f, h):
fpp, fpm, fmp, fmm = [f(i*h,j*h) for i,j in [(1,1),(1,-1),(-1,1),(-1,-1)]]
fpp = (fpp-fpm-fmp+fmm)/(4*h*h)
return fpp
def f2d4p(f, h, f0 = None):
if f0 == None:
fpp, fp0, f0p, f0 = [f(i*h,j*h) for i,j in [(1,1),(1,0),(0,1),(0,0)]]
else:
fpp, fp0, f0p = [f(i*h,j*h) for i,j in [(1,1),(1,0),(0,1)]]
fpp = (fpp-fp0-f0p+f0)/(h*h)
return fpp
for d in self.objfiles:
logger.info("\rNow working on" + str(d) + 50*' ' + '\r')
if wq == None:
x = self.driver(mvals, d)
grad = np.zeros(n)
hdiag = np.zeros(n)
hess = np.zeros((n,n))
apath = os.path.join(self.tdir, d, "current")
x = float(open(os.path.join(apath,'objective.out')).readlines()[0].split()[1])*self.factor
for p in range(self.FF.np):
if self.callderivs[d][p]:
def reader(mvals_,h):
apath = os.path.join(self.tdir, d, str(p), str(h))
answer = float(open(os.path.join(apath,'objective.out')).readlines()[0].split()[1])*self.factor
return answer
if AHess:
if wq != None:
apath = os.path.join(self.tdir, d, "current")
x = float(open(os.path.join(apath,'objective.out')).readlines()[0].split()[1])*self.factor
grad[p], hdiag[p] = f12d3p(fdwrap(reader, mvals, p, h=self.h), h = self.h, f0 = x)
else:
grad[p], hdiag[p] = f12d3p(fdwrap(self.driver, mvals, p, d=d), h = self.h, f0 = x)
hess[p,p] = hdiag[p]
elif AGrad:
if self.bidirect:
if wq != None:
apath = os.path.join(self.tdir, d, "current")
x = float(open(os.path.join(apath,'objective.out')).readlines()[0].split()[1])*self.factor
grad[p], _ = f12d3p(fdwrap(reader, mvals, p, h=self.h), h = self.h, f0 = x)
else:
grad[p], _ = f12d3p(fdwrap(self.driver, mvals, p, d=d), h = self.h, f0 = x)
else:
if wq != None:
# Since the calculations are submitted as 3-point finite difference, this part of the code
# actually only reads from half of the completed calculations.
grad[p] = f1d2p(fdwrap(reader, mvals, p, h=self.h), h = self.h, f0 = x)
else:
grad[p] = f1d2p(fdwrap(self.driver, mvals, p, d=d), h = self.h, f0 = x)
self.objd[d] = x
self.gradd[d] = grad
self.hdiagd[d] = hdiag
X += x
G += grad
#H += np.diag(hdiag)
H += hess
if not in_fd():
self.objective = X
self.objvals = self.objd
# print self.objd
# print self.gradd
# print self.hdiagd
if float('Inf') in pvals:
return {'X' : 1e10, 'G' : G, 'H' : H}
return {'X' : X, 'G' : G, 'H' : H}
def submit_jobs(self, mvals, AGrad=True, AHess=True):
# This routine is called by Objective.stage() will run before "get".
# It submits the jobs to the Work Queue and the stage() function will wait for jobs to complete.
#
self.tdir = os.getcwd()
wq = getWorkQueue()
if wq is None:
return
def submit_psi(this_apath, dname, these_mvals):
""" Create a grid file and a psi4 input file in the absolute path and submit it to the work queue. """
cwd = os.getcwd()
if not os.path.exists(this_apath): os.makedirs(this_apath)
os.chdir(this_apath)
self.FF.make(these_mvals)
o = wopen('objective.dat')
for line in self.objfiles[d]:
s = line.split()
if len(s) > 2 and s[0] == 'path' and s[1] == '=':
print("path = '%s'" % os.getcwd(), file=o)
elif len(s) > 2 and s[0] == 'set' and s[1] == 'objective_path':
print("opath = '%s'" % os.getcwd(), file=o)
print("set objective_path $opath", file=o)
else:
print(line, end=' ', file=o)
o.close()
os.system("rm -f objective.out")
if wq is None:
logger.info("There is no Work Queue!!!\n")
sys.exit()
else:
input_files = [(os.path.join(this_apath, i), i)
for i in glob.glob("*")]
input_files += [(os.path.join(self.root, self.tgtdir, dname,
"build.dat"), "build.dat")]
input_files += [(os.path.join(
os.path.split(__file__)[0], "data",
"run_psi_rdvr3_objective.sh"),
"run_psi_rdvr3_objective.sh")]
logger.info("\r")
queue_up_src_dest(
wq,
"sh run_psi_rdvr3_objective.sh -c %s &> run_psi_rdvr3_objective.log"
% os.path.join(self.root, self.tgtdir, dname),
input_files=input_files,
output_files=[
(os.path.join(this_apath, i), i)
for i in ["run_psi_rdvr3_objective.log", "output.dat"]
],
verbose=False)
os.chdir(cwd)
for d in self.objfiles:
logger.info("\rNow working on" + str(d) + 50 * ' ' + '\r')
odir = os.path.join(os.getcwd(), d)
#if os.path.exists(odir):
# shutil.rmtree(odir)
if not os.path.exists(odir): os.makedirs(odir)
apath = os.path.join(odir, "current")
submit_psi(apath, d, mvals)
for p in range(self.FF.np):
def subjob(mvals_, h):
apath = os.path.join(odir, str(p), str(h))
submit_psi(apath, d, mvals_)
#logger.info("Will set up a job for %s, parameter %i\n" % (d, p))
return 0.0
if self.callderivs[d][p]:
if AHess:
f12d3p(fdwrap(subjob, mvals, p, h=self.h),
h=self.h,
f0=0.0)
elif AGrad:
if self.bidirect:
f12d3p(fdwrap(subjob, mvals, p, h=self.h),
h=self.h,
f0=0.0)
else:
f1d2p(fdwrap(subjob, mvals, p, h=self.h),
h=self.h,
f0=0.0)
def get(self, mvals, AGrad=False, AHess=False):
"""
LPW 04-17-2013
This subroutine builds the objective function from Psi4.
@param[in] mvals Mathematical parameter values
@param[in] AGrad Switch to turn on analytic gradient
@param[in] AHess Switch to turn on analytic Hessian
@return Answer Contribution to the objective function
"""
Answer = {}
Fac = 1000000
n = len(mvals)
X = 0.0
G = np.zeros(n)
H = np.zeros((n, n))
pvals = self.FF.make(mvals)
self.tdir = os.getcwd()
self.objd = OrderedDict()
self.gradd = OrderedDict()
self.hdiagd = OrderedDict()
wq = getWorkQueue()
def fdwrap2(func, mvals0, pidx, qidx, key=None, **kwargs):
def func2(arg1, arg2):
mvals = list(mvals0)
mvals[pidx] += arg1
mvals[qidx] += arg2
logger.info("\rfdwrap2:" + func.__name__ +
"[%i] = % .1e , [%i] = % .1e" %
(pidx, arg1, qidx, arg2) + ' ' * 50)
if key is not None:
return func(mvals, **kwargs)[key]
else:
return func(mvals, **kwargs)
return func2
def f2d5p(f, h):
fpp, fpm, fmp, fmm = [
f(i * h, j * h)
for i, j in [(1, 1), (1, -1), (-1, 1), (-1, -1)]
]
fpp = (fpp - fpm - fmp + fmm) / (4 * h * h)
return fpp
def f2d4p(f, h, f0=None):
if f0 is None:
fpp, fp0, f0p, f0 = [
f(i * h, j * h)
for i, j in [(1, 1), (1, 0), (0, 1), (0, 0)]
]
else:
fpp, fp0, f0p = [
f(i * h, j * h) for i, j in [(1, 1), (1, 0), (0, 1)]
]
fpp = (fpp - fp0 - f0p + f0) / (h * h)
return fpp
for d in self.objfiles:
logger.info("\rNow working on" + str(d) + 50 * ' ' + '\r')
if wq is None:
x = self.driver(mvals, d)
grad = np.zeros(n)
hdiag = np.zeros(n)
hess = np.zeros((n, n))
apath = os.path.join(self.tdir, d, "current")
x = float(
open(os.path.join(
apath,
'objective.out')).readlines()[0].split()[1]) * self.factor
for p in range(self.FF.np):
if self.callderivs[d][p]:
def reader(mvals_, h):
apath = os.path.join(self.tdir, d, str(p), str(h))
answer = float(
open(os.path.join(apath, 'objective.out')).
readlines()[0].split()[1]) * self.factor
return answer
if AHess:
if wq is not None:
apath = os.path.join(self.tdir, d, "current")
x = float(
open(os.path.join(apath, 'objective.out')).
readlines()[0].split()[1]) * self.factor
grad[p], hdiag[p] = f12d3p(fdwrap(reader,
mvals,
p,
h=self.h),
h=self.h,
f0=x)
else:
grad[p], hdiag[p] = f12d3p(fdwrap(self.driver,
mvals,
p,
d=d),
h=self.h,
f0=x)
hess[p, p] = hdiag[p]
elif AGrad:
if self.bidirect:
if wq is not None:
apath = os.path.join(self.tdir, d, "current")
x = float(
open(os.path.join(apath, 'objective.out')).
readlines()[0].split()[1]) * self.factor
grad[p], _ = f12d3p(fdwrap(reader,
mvals,
p,
h=self.h),
h=self.h,
f0=x)
else:
grad[p], _ = f12d3p(fdwrap(self.driver,
mvals,
p,
d=d),
h=self.h,
f0=x)
else:
if wq is not None:
# Since the calculations are submitted as 3-point finite difference, this part of the code
# actually only reads from half of the completed calculations.
grad[p] = f1d2p(fdwrap(reader,
mvals,
p,
h=self.h),
h=self.h,
f0=x)
else:
grad[p] = f1d2p(fdwrap(self.driver,
mvals,
p,
d=d),
h=self.h,
f0=x)
self.objd[d] = x
self.gradd[d] = grad
self.hdiagd[d] = hdiag
X += x
G += grad
#H += np.diag(hdiag)
H += hess
if not in_fd():
self.objective = X
self.objvals = self.objd
# print self.objd
# print self.gradd
# print self.hdiagd
if float('Inf') in pvals:
return {'X': 1e10, 'G': G, 'H': H}
return {'X': X, 'G': G, 'H': H}
def submit_jobs(self, mvals, AGrad=True, AHess=True):
# This routine is called by Objective.stage() will run before "get".
# It submits the jobs to the Work Queue and the stage() function will wait for jobs to complete.
#
self.tdir = os.getcwd()
wq = getWorkQueue()
if wq == None:
return
def submit_psi(this_apath, mname, these_mvals):
""" Create a grid file and a psi4 input file in the absolute path and submit it to the work queue. """
cwd = os.getcwd()
if not os.path.exists(this_apath) : os.makedirs(this_apath)
os.chdir(this_apath)
self.FF.make(these_mvals)
o = wopen('objective.dat')
for line in self.objfiles[d]:
s = line.split()
if len(s) > 2 and s[0] == 'path' and s[1] == '=':
print >> o, "path = '%s'" % os.getcwd()
elif len(s) > 2 and s[0] == 'set' and s[1] == 'objective_path':
print >> o, "opath = '%s'" % os.getcwd()
print >> o, "set objective_path $opath"
else:
print >> o, line,
o.close()
os.system("rm -f objective.out")
if wq == None:
logger.info("There is no Work Queue!!!\n")
sys.exit()
else:
input_files = [(os.path.join(this_apath, i), i) for i in glob.glob("*")]
# input_files += [(os.path.join(self.tgtdir,d,"build.dat"), "build.dat")]
input_files += [(os.path.join(os.path.split(__file__)[0],"data","run_psi_rdvr3_objective.sh"), "run_psi_rdvr3_objective.sh")]
logger.info("\r")
queue_up_src_dest(wq,"sh run_psi_rdvr3_objective.sh %s &> run_psi_rdvr3_objective.log" % mname,
input_files=input_files,
output_files=[(os.path.join(this_apath, i),i) for i in ["run_psi_rdvr3_objective.log", "output.dat"]], verbose=False)
os.chdir(cwd)
for d in self.objfiles:
logger.info("\rNow working on" + str(d) + 50*' ' + '\r')
odir = os.path.join(os.getcwd(),d)
#if os.path.exists(odir):
# shutil.rmtree(odir)
if not os.path.exists(odir): os.makedirs(odir)
apath = os.path.join(odir, "current")
submit_psi(apath, d, mvals)
for p in range(self.FF.np):
def subjob(mvals_,h):
apath = os.path.join(odir, str(p), str(h))
submit_psi(apath, d, mvals_)
#logger.info("Will set up a job for %s, parameter %i\n" % (d, p))
return 0.0
if self.callderivs[d][p]:
if AHess:
f12d3p(fdwrap(subjob, mvals, p, h=self.h), h = self.h, f0 = 0.0)
elif AGrad:
if self.bidirect:
f12d3p(fdwrap(subjob, mvals, p, h=self.h), h = self.h, f0 = 0.0)
else:
f1d2p(fdwrap(subjob, mvals, p, h=self.h), h = self.h, f0 = 0.0)
def calc_wq_new(self, coords, dirname):
# Run TeraChem
wq = getWorkQueue()
if not os.path.exists(dirname): os.makedirs(dirname)
scrdir = os.path.join(dirname, 'scr')
if not os.path.exists(scrdir): os.makedirs(scrdir)
guesses = []
have_guess = False
unrestricted = self.tcin['method'][0] == 'u'
if unrestricted:
guessfnms = ['ca0', 'cb0']
else:
guessfnms = ['c0']
for f in ['c0', 'ca0', 'cb0']:
if f not in guessfnms: continue
if os.path.exists(os.path.join(dirname, 'scr', f)):
shutil.move(os.path.join(dirname, 'scr', f),
os.path.join(dirname, f))
guesses.append(f)
if os.path.exists(os.path.join(dirname, f)):
if f not in guesses: guesses.append(f)
# Check if all the appropriate guess files have been found
# and moved to "dirname"
have_guess = (guesses == guessfnms)
# This is for when we start geometry optimizations
# and we have a guess prepped and ready to go.
if not have_guess and 'guess' in self.tcin:
for f in self.tcin['guess'].split():
if os.path.exists(f):
shutil.copy2(f, dirname)
guesses.append(f)
have_guess = True
else:
del self.tcin['guess']
have_guess = False
break
self.tcin['coordinates'] = 'start.xyz'
self.tcin['run'] = 'gradient'
# For queueing up jobs, delete GPU key and let the worker decide
self.tcin['gpus'] = None
if have_guess:
self.tcin['guess'] = ' '.join(guesses)
self.tcin['purify'] = 'no'
self.tcin['mixguess'] = "0.0"
tcopts = edit_tcin(fout="%s/run.in" % dirname, options=self.tcin)
# Convert coordinates back to the xyz file
self.M.xyzs[0] = coords.reshape(-1, 3) * 0.529177
self.M[0].write(os.path.join(dirname, 'start.xyz'))
in_files = [('%s/run.in' % dirname, 'run.in'),
('%s/start.xyz' % dirname, 'start.xyz')]
out_files = [('%s/run.out' % dirname, 'run.out')]
if have_guess:
for g in guesses:
in_files.append((os.path.join(dirname, g), g))
for g in guessfnms:
out_files.append((os.path.join(dirname, 'scr',
g), os.path.join('scr', g)))
queue_up_src_dest(wq,
"%s/runtc run.in &> run.out" % rootdir,
in_files,
out_files,
verbose=False)