def start_minimization(self, which_min):
job = {}
saddle_path = os.path.join(config.path_incomplete, self.finished_saddle_name)
mode_file = open(os.path.join(saddle_path, "mode.dat"))
mode = io.load_mode(mode_file)
mode_file.close()
reactant_file = open(os.path.join(saddle_path, "saddle.con"))
reactant = io.loadcon(reactant_file)
reactant_file.close()
if which_min == "min2":
mode = -mode
reactant.r += config.process_search_minimization_offset*mode
reactIO = StringIO.StringIO()
io.savecon(reactIO, reactant)
job['pos.con'] = reactIO
ini_changes = [ ('Main', 'job', 'minimization') ]
job['config.ini'] = io.modify_config(config.config_path, ini_changes)
return job
def start_minimization(self, which_min):
job = {}
saddle_path = os.path.join(config.path_incomplete,
self.finished_saddle_name)
mode_file = open(os.path.join(saddle_path, "mode.dat"))
mode = io.load_mode(mode_file)
mode_file.close()
reactant_file = open(os.path.join(saddle_path, "saddle.con"))
reactant = io.loadcon(reactant_file)
reactant_file.close()
if which_min == "min2":
mode = -mode
reactant.r += config.process_search_minimization_offset * mode
reactIO = StringIO.StringIO()
io.savecon(reactIO, reactant)
job['pos.con'] = reactIO
ini_changes = [('Main', 'job', 'minimization')]
job['config.ini'] = io.modify_config(config.config_path, ini_changes)
return job
def make_suggestion(self):
""" Makes a saddle suggestion and returns True.
If no more saddle suggestions are possible
(end of process table has been reached), returns False. """
# If we've gone through all the possible processes
if self.process_number >= self.num_procs:
self.write_recycling_metadata()
return None, None
# Make a fresh copy of the "saddle" we're going to send,
# based on the current reactant.
saddle = self.curr_reactant.copy()
# Determine what happens in the reference state saddle
# for the current process number.
process_saddle = self.ref_state.get_process_saddle(self.process_number)
process_mode = self.ref_state.get_process_mode(self.process_number)
# Now, for all the things that did *not* move getting to this state,
# suggest this particular process's position to them.
for i in self.unmoved:
saddle.r[i] = process_saddle.r[i]
self.mode[i] = process_mode[i]
# And, for all the things that *did* move getting to this state,
# suggest the *motion* that they had available before to the
# current position they're in.
for i in self.moved:
movement = process_saddle.r[i] - self.ref_reactant.r[i]
saddle.r[i] += movement
self.mode[i] = process_mode[i]
# Save suggestions?
if self.save:
save_path = os.path.join(self.current_state.path,
"saddle_suggestions")
if not os.path.isdir(save_path):
os.mkdir(save_path)
fo = open(os.path.join(save_path, "proc_%d" % self.process_number),
"w")
io.savecon(fo, saddle)
fo.close()
# Make a note of the fact that we've tried to recycle another saddle.
self.process_number += 1
self.write_recycling_metadata()
# Note: Uncomment the final return values to also return the list of indices of atoms
# which are not in the hole and in the hole. No change should need to be made to the
# line in akmc.py which calls this function (unless akmc.py wants them as well).
return saddle.copy(), self.mode.copy()
def make_suggestion(self):
""" Makes a saddle suggestion and returns True.
If no more saddle suggestions are possible
(end of process table has been reached), returns False. """
# If we've gone through all the possible processes
if self.process_number >= self.num_procs:
self.write_recycling_metadata()
return None, None
# Make a fresh copy of the "saddle" we're going to send,
# based on the current reactant.
saddle = self.curr_reactant.copy()
# Determine what happens in the reference state saddle
# for the current process number.
process_saddle = self.ref_state.get_process_saddle(self.process_number)
process_mode = self.ref_state.get_process_mode(self.process_number)
# Now, for all the things that did *not* move getting to this state,
# suggest this particular process's position to them.
for i in self.unmoved:
saddle.r[i] = process_saddle.r[i]
self.mode[i] = process_mode[i]
# And, for all the things that *did* move getting to this state,
# suggest the *motion* that they had available before to the
# current position they're in.
for i in self.moved:
movement = process_saddle.r[i] - self.ref_reactant.r[i]
saddle.r[i] += movement
self.mode[i] = process_mode[i]
# Save suggestions?
if self.save:
save_path = os.path.join(self.current_state.path, "saddle_suggestions")
if not os.path.isdir(save_path):
os.mkdir(save_path)
fo = open(os.path.join(save_path, "proc_%d" %self.process_number), "w")
io.savecon(fo, saddle)
fo.close()
# Make a note of the fact that we've tried to recycle another saddle.
self.process_number += 1
self.write_recycling_metadata()
# Note: Uncomment the final return values to also return the list of indices of atoms
# which are not in the hole and in the hole. No change should need to be made to the
# line in akmc.py which calls this function (unless akmc.py wants them as well).
return saddle.copy(), self.mode.copy()
def start_search(self):
job = {}
dispIO = StringIO.StringIO()
io.savecon(dispIO, self.displacement)
job['displacement.con'] = dispIO
modeIO = StringIO.StringIO()
io.save_mode(modeIO, self.mode)
job['direction.dat'] = modeIO
reactIO = StringIO.StringIO()
io.savecon(reactIO, self.reactant)
job['pos.con'] = reactIO
ini_changes = [ ('Main', 'job', 'saddle_search') ]
job['config.ini'] = io.modify_config(config.config_path, ini_changes)
return job
def start_search(self):
job = {}
dispIO = StringIO.StringIO()
io.savecon(dispIO, self.displacement)
job['displacement.con'] = dispIO
modeIO = StringIO.StringIO()
io.save_mode(modeIO, self.mode)
job['direction.dat'] = modeIO
reactIO = StringIO.StringIO()
io.savecon(reactIO, self.reactant)
job['pos.con'] = reactIO
ini_changes = [('Main', 'job', 'saddle_search')]
job['config.ini'] = io.modify_config(config.config_path, ini_changes)
return job
def make_searches(comm, current_state, wuid):
reactant = current_state.get_reactant()
#XXX:what if the user changes the bundle size?
num_in_buffer = comm.get_queue_size() * config.comm_job_bundle_size
logger.info("Queue contains %i searches" % num_in_buffer)
num_to_make = max(config.comm_job_buffer_size - num_in_buffer, 0)
logger.info("Making %i searches" % num_to_make)
if num_to_make == 0:
return wuid
searches = []
invariants = {}
reactIO = StringIO()
io.savecon(reactIO, reactant)
# Merge potential files into invariants
#XXX: Should this be in our "science" maybe the communicator should
# handle this.
invariants = dict(invariants, **io.load_potfiles(config.path_pot))
searches = []
for i in range(num_to_make):
search = {}
search['id'] = "%d_%d" % (current_state.number, wuid)
search['pos.con'] = reactIO
ini_changes = [
('Main', 'job', 'parallel_replica'),
('Main', 'random_seed', str(int(numpy.random.random() * 10**9))),
]
search['config.ini'] = io.modify_config(config.config_path,
ini_changes)
searches.append(search)
wuid += 1
comm.submit_jobs(searches, invariants)
logger.info("Created " + str(num_to_make) + " searches")
return wuid
def make_searches(comm, current_state, wuid):
reactant = current_state.get_reactant()
#XXX:what if the user changes the bundle size?
num_in_buffer = comm.get_queue_size()*config.comm_job_bundle_size
logger.info("Queue contains %i searches" % num_in_buffer)
num_to_make = max(config.comm_job_buffer_size - num_in_buffer, 0)
logger.info("Making %i searches" % num_to_make)
if num_to_make == 0:
return wuid
searches = []
invariants = {}
reactIO = StringIO()
io.savecon(reactIO, reactant)
# Merge potential files into invariants
#XXX: Should this be in our "science" maybe the communicator should
# handle this.
invariants = dict(invariants, **io.load_potfiles(config.path_pot))
searches = []
for i in range(num_to_make):
search = {}
search['id'] = "%d_%d" % (current_state.number, wuid)
search['pos.con'] = reactIO
ini_changes = [
('Main', 'job', 'parallel_replica'),
('Main', 'random_seed',
str(int(numpy.random.random()*10**9))),
]
search['config.ini'] = io.modify_config(config.config_path, ini_changes)
searches.append(search)
wuid += 1
comm.submit_jobs(searches, invariants)
logger.info("Created " + str(num_to_make) + " searches")
return wuid
def make_jobs(self):
#XXX:what if the user changes the bundle size?
num_in_buffer = self.comm.get_queue_size()*config.comm_job_bundle_size
logger.info("Queue contains %i searches" % num_in_buffer)
num_to_make = max(config.comm_job_buffer_size - num_in_buffer, 0)
logger.info("Making %i process searches" % num_to_make)
if num_to_make == 0:
return
searches = []
invariants = {}
reactIO = StringIO.StringIO()
io.savecon(reactIO, self.reactant)
file_permission = os.stat("pos.con").st_mode
invariants['pos.con'] = (reactIO, file_permission)
t1 = time()
if config.saddle_method == 'dynamics' and \
config.recycling_on and \
config.disp_moved_only and \
self.state.number != 0:
moved_atoms = self.recycler.process_atoms
mass_weights = self.reactant.mass.copy()
mass_weights *= config.recycling_mass_weight_factor
for i in range(len(self.reactant)):
if i in moved_atoms:
mass_weights[i] = self.reactant.mass[i]
weightsIO = StringIO.StringIO()
numpy.savetxt(weightsIO, mass_weights)
file_permission = os.stat("masses.dat").st_mode
invariants['masses.dat'] = (weightsIO, file_permission)
# Merge potential files into invariants
invariants = dict(invariants, **io.load_potfiles(config.path_pot))
for i in range(num_to_make):
search = {}
# The search dictionary contains the following key-value pairs:
# id - CurrentState_WUID
# displacement - an atoms object containing the point the saddle search will start at
# mode - an Nx3 numpy array containing the initial mode
search['id'] = "%d_%d" % (self.state.number, self.wuid)
displacement, mode, disp_type = self.generate_displacement()
self.job_table.add_row( {'state':self.state.number,
'wuid':self.wuid,
'type':disp_type } )
ini_changes = [ ('Main', 'job', 'process_search'),
('Main', 'random_seed',
str(int(numpy.random.random()*10**9))),
]
# if we are recycling a saddle, but using "dynamics saddle search" we need
# to switch to min_mode searches
if config.saddle_method == 'dynamics' and disp_type != 'dynamics':
ini_changes.append( ('Saddle Search', 'method', 'min_mode') )
search['config.ini'] = io.modify_config(config.config_path, ini_changes)
if displacement:
dispIO = StringIO.StringIO()
io.savecon(dispIO, displacement)
search['displacement.con'] = dispIO
modeIO = StringIO.StringIO()
io.save_mode(modeIO, mode)
search['direction.dat'] = modeIO
searches.append(search)
self.wuid += 1
# eager write
self.save_wuid()
if config.recycling_on and self.nrecycled > 0:
logger.info("Recycled %i saddles" % self.nrecycled)
try:
self.comm.submit_jobs(searches, invariants)
t2 = time()
logger.info( "Created " + str(len(searches)) + " searches")
logger.debug( "Created " + str(num_to_make/(t2-t1)) + " searches per second")
except:
logger.exception("Failed to submit searches")
self.job_table.write()
def make_jobs(self):
#XXX:what if the user changes the bundle size?
num_in_buffer = self.comm.get_queue_size(
) * config.comm_job_bundle_size
logger.info("Queue contains %i searches" % num_in_buffer)
num_to_make = max(config.comm_job_buffer_size - num_in_buffer, 0)
logger.info("Making %i process searches" % num_to_make)
if num_to_make == 0:
return
searches = []
invariants = {}
reactIO = StringIO.StringIO()
io.savecon(reactIO, self.reactant)
file_permission = os.stat("pos.con").st_mode
invariants['pos.con'] = (reactIO, file_permission)
t1 = time()
if config.saddle_method == 'dynamics' and \
config.recycling_on and \
config.disp_moved_only and \
self.state.number != 0:
moved_atoms = self.recycler.process_atoms
mass_weights = self.reactant.mass.copy()
mass_weights *= config.recycling_mass_weight_factor
for i in range(len(self.reactant)):
if i in moved_atoms:
mass_weights[i] = self.reactant.mass[i]
weightsIO = StringIO.StringIO()
numpy.savetxt(weightsIO, mass_weights)
file_permission = os.stat("masses.dat").st_mode
invariants['masses.dat'] = (weightsIO, file_permission)
# Merge potential files into invariants
invariants = dict(invariants, **io.load_potfiles(config.path_pot))
for i in range(num_to_make):
search = {}
# The search dictionary contains the following key-value pairs:
# id - CurrentState_WUID
# displacement - an atoms object containing the point the saddle search will start at
# mode - an Nx3 numpy array containing the initial mode
search['id'] = "%d_%d" % (self.state.number, self.wuid)
displacement, mode, disp_type = self.generate_displacement()
self.job_table.add_row({
'state': self.state.number,
'wuid': self.wuid,
'type': disp_type
})
ini_changes = [
('Main', 'job', 'process_search'),
('Main', 'random_seed',
str(int(numpy.random.random() * 10**9))),
]
# if we are recycling a saddle, but using "dynamics saddle search" we need
# to switch to min_mode searches
if config.saddle_method == 'dynamics' and disp_type != 'dynamics':
ini_changes.append(('Saddle Search', 'method', 'min_mode'))
search['config.ini'] = io.modify_config(config.config_path,
ini_changes)
if displacement:
dispIO = StringIO.StringIO()
io.savecon(dispIO, displacement)
search['displacement.con'] = dispIO
modeIO = StringIO.StringIO()
io.save_mode(modeIO, mode)
search['direction.dat'] = modeIO
searches.append(search)
self.wuid += 1
# eager write
self.save_wuid()
if config.recycling_on and self.nrecycled > 0:
logger.info("Recycled %i saddles" % self.nrecycled)
try:
self.comm.submit_jobs(searches, invariants)
t2 = time()
logger.info("Created " + str(len(searches)) + " searches")
logger.debug("Created " + str(num_to_make / (t2 - t1)) +
" searches per second")
except:
logger.exception("Failed to submit searches")
self.job_table.write()
if 'bcc' in sys.argv:
ids = atoms.cnat(p, 3.5)
for i in range(len(ids)):
if ids[i] == 5:
rejects.append(i)
if 'a15' in sys.argv:
ids = atoms.cnat(p, 3.5)
for i in range(len(ids)):
if ids[i] in [1, 2]:
rejects.append(i)
rejects = list(set(rejects))
newp = atoms.Atoms(len(p) - len(rejects))
newp.box = p.box
index = 0
for i in range(len(p)):
if i not in rejects:
newp.r[index] = p.r[i]
newp.free[index] = p.free[i]
newp.names[index] = p.names[i]
newp.mass[index] = p.mass[i]
index += 1
filtered.append(newp)
io.savecon(sys.argv[2], filtered[0], 'w')
for p in filtered[1:]:
io.savecon(sys.argv[2], p, 'a')
