def meta_get(self, mvals, AGrad=False, AHess=False, customdir=None):
"""
Wrapper around the get function.
Create the directory for the target, and then calls 'get'.
If we are reading existing data, go into the appropriate read directory and call read() instead.
The 'get' method should not worry about the directory that it's running in.
"""
## Directory of the current iteration; if not None, then the simulation runs under
## temp/target_name/iteration_number
## The 'customdir' is customizable and can go below anything
cwd = os.getcwd()
absgetdir = os.path.join(self.root,self.tempdir)
if Counter() is not None:
# Not expecting more than ten thousand iterations
if Counter() > 10000:
logger.error('Cannot handle more than 10000 iterations due to current directory structure. Consider revising code.\n')
raise RuntimeError
iterdir = "iter_%04i" % Counter()
absgetdir = os.path.join(absgetdir,iterdir)
if customdir is not None:
absgetdir = os.path.join(absgetdir,customdir)
if not os.path.exists(absgetdir):
os.makedirs(absgetdir)
os.chdir(absgetdir)
self.link_from_tempdir(absgetdir)
self.rundir = absgetdir.replace(self.root+'/','')
## Read existing information from disk (i.e. when recovering an aborted run)
# Note that reading information is not supported for custom folders (e.g. microiterations during search)
if self.rd != None and (not self.evaluated) and self.read_objective and customdir == None:
os.chdir(self.absrd())
logger.info("Reading objective function information from %s\n" % os.getcwd())
Answer = self.read(mvals, AGrad, AHess)
os.chdir(absgetdir)
else:
## Evaluate the objective function.
Answer = self.get(mvals, AGrad, AHess)
if self.write_objective:
forcebalance.nifty.lp_dump(Answer, 'objective.p')
## Save the force field files to this directory, so that it
## reflects the objective function and properties that were
## printed out.
if not in_fd_srch():
self.FF.make(mvals)
os.chdir(cwd)
return Answer
def stage(self, mvals, AGrad=False, AHess=False, customdir=None, firstIteration=False):
"""
Stages the directory for the target, and then launches Work Queue processes if any.
The 'get' method should not worry about the directory that it's running in.
"""
if self.sleepy > 0:
logger.info("Sleeping for %i seconds as directed...\n" % self.sleepy)
time.sleep(self.sleepy)
## Directory of the current iteration; if not None, then the simulation runs under
## temp/target_name/iteration_number
## The 'customdir' is customizable and can go below anything
cwd = os.getcwd()
absgetdir = os.path.join(self.root,self.tempdir)
if Counter() is not None:
## Not expecting more than ten thousand iterations
iterdir = "iter_%04i" % Counter()
absgetdir = os.path.join(absgetdir,iterdir)
if customdir is not None:
absgetdir = os.path.join(absgetdir,customdir)
## Go into the directory where get() will be executed.
if not os.path.exists(absgetdir):
os.makedirs(absgetdir)
os.chdir(absgetdir)
self.link_from_tempdir(absgetdir)
## Write mathematical parameters to file; will be used to checkpoint calculation.
if not in_fd_srch():
np.savetxt('mvals.txt', mvals)
## Read in file that specifies which derivatives may be skipped.
if Counter() >= self.zerograd and self.zerograd >= 0:
self.read_0grads()
self.rundir = absgetdir.replace(self.root+'/','')
## Submit jobs to the Work Queue.
if self.rd == None or (not firstIteration):
self.submit_jobs(mvals, AGrad, AHess)
elif customdir != None:
# Allows us to submit micro-iteration jobs for remote targets
self.submit_jobs(mvals, AGrad, AHess)
os.chdir(cwd)
return
