def compile_training_set(path="./training_sets"):
if path.endswith("/"): path = path[:-1]
if not os.path.exists(path):
raise Exception("Unable to find training set directory")
frames = []
i = 0
while os.path.exists(path+"/"+str(i)+".cml"):
atoms = files.read_cml(path+"/"+str(i)+".cml", return_molecules=False, allow_errors=True, test_charges=False)[0]
frames.append(atoms)
i += 1
files.write_xyz(frames, "full_training_set")
def calculate(coords):
coord_count = 0
for s in NEB.states[1:-1]:
for a in s:
a.x, a.y, a.z = coords[coord_count], coords[coord_count+1], coords[coord_count+2]
coord_count += 3
#start DFT jobs
running_jobs = []
for i,state in enumerate(NEB.states[1:-1]):
guess = '' if NEB.step==0 else ' Guess=Read'
running_jobs.append( job('%s-%d-%d'%(NEB.name,NEB.step,i), NEB.theory+' Force'+guess, state, queue=queue, force=True, previous=('%s-%d-%d'%(NEB.name,NEB.step-1,i)) if NEB.step>0 else None, extra_section=extra_section) )
#wait for jobs to finish
for j in running_jobs: j.wait()
#get forces and energies from DFT calculations
energies = []
for i,state in enumerate(NEB.states[1:-1]):
try:
new_energy, new_atoms = parse_atoms('%s-%d-%d'%(NEB.name,NEB.step,i))
except:
print 'Job failed: %s-%d-%d'%(NEB.name,NEB.step,i); exit()
energies.append(new_energy)
for a,b in zip(state, new_atoms):
a.fx = b.fx; a.fy = b.fy; a.fz = b.fz
dft_energies = copy.deepcopy(energies)
#add spring forces to atoms
for i,state in enumerate(NEB.states[1:-1]):
for j,b in enumerate(state):
a,c = NEB.states[i-1][j], NEB.states[i+1][j]
if j in spring_atoms:
b.fx += NEB.k*(a.x-b.x) + NEB.k*(c.x-b.x)
b.fy += NEB.k*(a.y-b.y) + NEB.k*(c.y-b.y)
b.fz += NEB.k*(a.z-b.z) + NEB.k*(c.z-b.z)
energies[i] += 0.5*NEB.k*(utils.dist_squared(a,b) + utils.dist_squared(b,c))
#set error
NEB.error = sum(energies)
#set forces
NEB.forces = []
for state in NEB.states[1:-1]:
for a in state:
NEB.forces += [-a.fx, -a.fy, -a.fz] #derivative of the error
#increment step
NEB.step += 1
#write to xyz file
NEB.xyz = open(name+'.xyz', 'w')
for state in NEB.states:
files.write_xyz(state, NEB.xyz)
NEB.xyz.close()
#print data
print NEB.step, NEB.error, ('%10.7g '*len(dft_energies)) % tuple(dft_energies)
def compile_training_set(path="./training_set", extra_parameters={}):
if path.endswith("/"):
path = path[:-1]
if not os.path.exists(path):
raise Exception("Unable to find training set directory")
frames = []
i = 0
while os.path.exists(path + "/" + str(i) + ".cml"):
atoms = files.read_cml(path + "/" + str(i) + ".cml",
return_molecules=False,
allow_errors=True,
test_charges=False,
extra_parameters=extra_parameters)[0]
frames.append(atoms)
i += 1
files.write_xyz(frames, "full_training_set")
def method_CLANCELOT(opt_method="LBFGS"):
from files import read_xyz, write_xyz
import neb
from units import convert, convert_energy
FPTR = "./../xyz/CNH_HCN.xyz"
frames = read_xyz(FPTR)
route = '! HF-3c'
if RIGID_ROTATION:
is_on = "ON"
else:
is_on = "OFF"
print("\nRUNNING CLANCELOT SIMULATION WITH RIGID_ROTATION %s...\n" % is_on)
run_name = 'CNH_HCN_c_' + opt_method
new_opt_params = {
'step_size': ALPHA,
'step_size_adjustment': 0.5,
'max_step': MAX_STEP,
'linesearch': 'backtrack',
'accelerate': True,
'reset_step_size': 5,
'g_rms': convert("eV/Ang", "Ha/Ang", 0.03),
'g_max': convert("eV/Ang", "Ha/Ang", FMAX)
}
opt = neb.NEB(run_name,
frames,
route,
k=convert_energy("eV", "Ha", 0.1),
opt=opt_method,
new_opt_params=new_opt_params)
output = opt.optimize()
frames = output[-1]
write_xyz(frames, "CNH_HCN_opt_%s" % opt_method)
print("\nDONE WITH CLANCELOT SIMULATION...\n")
def pickle_training_set(run_name,
training_sets_folder="training_set",
pickle_file_name="training_set",
high_energy_cutoff=500.0,
system_x_offset=1000.0,
verbose=False,
extra_parameters={}):
"""
A function to pickle together the training set in a manner that is
readable for MCSMRFF. This is a single LAMMPs data file with each
training set offset alongst the x-axis by system_x_offset. The pickle
file, when read in later, holds a list of two objects. The first is
the entire system as described above. The second is a dictionary of all
molecules in the system, organized by composition.
**Parameters**
run_name: *str*
Name of final training set.
training_sets_folder: *str, optional*
Path to the folder where all the training set data is.
pickle_file_name: *str, optional*
A name for the pickle file and training set system.
high_energy_cutoff: *float, optional*
A cutoff for systems that are too large in energy, as MD is likely
never to sample them.
system_x_offset: *float, optional*
The x offset for the systems to be added by.
verbose: *bool, optional*
Whether to have additional stdout or not.
extra_parameters: *dict, optional*
A dictionaries for additional parameters that do not exist
in the default OPLSAA parameter file.
**Returns**
system: *System*
The entire training set system.
systems_by_composition: *dict, list, Molecule*
Each molecule organized in this hash table.
"""
# Take care of pickle file I/O
if training_sets_folder.endswith("/"):
training_sets_folder = training_sets_folder[:-1]
if pickle_file_name is not None and pickle_file_name.endswith(".pickle"):
pickle_file_name = pickle_file_name.split(".pickle")[0]
pfile = training_sets_folder + "/" + pickle_file_name + ".pickle"
sys_name = pickle_file_name
if os.path.isfile(pfile):
raise Exception("Pickled training set already exists!")
# Generate empty system for your training set
system = None
system = structures.System(box_size=[1e3, 100.0, 100.0], name=sys_name)
systems_by_composition = {}
# For each folder in the training_sets folder lets get the cml file we
# want and write the energies and forces for that file
for name in os.listdir(training_sets_folder):
# We'll read in any training subset that succeeded and print a warning
# on those that failed
try:
result = orca.read("%s/%s/%s.out" %
(training_sets_folder, name, name))
except IOError:
print(
"Warning - Training Subset %s not included as \
out file not found..." % name)
continue
# Check for convergence
if not result.converged:
print("Warning - Results for %s have not converged." % name)
continue
# Parse the force output and change units. In the case of no force
# found, do not use this set of data
try:
forces = orca.engrad_read("%s/%s/%s.orca.engrad" %
(training_sets_folder, name, name),
pos="Ang")[0]
# Convert force from Ha/Bohr to kcal/mol-Ang
def convert(x):
return units.convert_dist(
"Ang", "Bohr", units.convert_energy("Ha", "kcal", x))
for a, b in zip(result.atoms, forces):
a.fx, a.fy, a.fz = convert(b.fx), convert(b.fy), convert(b.fz)
except (IndexError, IOError):
print(
"Warning - Training Subset %s not included as \
results not found..." % name)
continue
# Get the bonding information
with_bonds = structures.Molecule("%s/%s/%s.cml" %
(training_sets_folder, name, name),
extra_parameters=extra_parameters,
allow_errors=True,
test_charges=False)
# Copy over the forces read in into the system that has the bonding
# information
for a, b in zip(with_bonds.atoms, result.atoms):
a.fx, a.fy, a.fz = b.fx, b.fy, b.fz
# sanity check on atom positions
if geometry.dist(a, b) > 1e-4:
raise Exception('Atoms are different:', (a.x, a.y, a.z),
(b.x, b.y, b.z))
# Rename and save energy
with_bonds.energy = result.energy
with_bonds.name = name
# Now, we read in all the potential three-body interactions that our
# training set takes into account. This will be in a 1D array
composition = ' '.join(sorted([a.element for a in result.atoms]))
if composition not in systems_by_composition:
systems_by_composition[composition] = []
systems_by_composition[composition].append(with_bonds)
# Generate:
# (1) xyz file of various systems as different time steps
# (2) system to simulate
xyz_atoms = []
to_delete = []
for i, composition in enumerate(systems_by_composition):
# Sort so that the lowest energy training subset is first
# in the system
systems_by_composition[composition].sort(key=lambda s: s.energy)
baseline_energy = systems_by_composition[composition][0].energy
# Offset the energies by the lowest energy, and convert energy units
for j, s in enumerate(systems_by_composition[composition]):
s.energy -= baseline_energy
s.energy = units.convert_energy("Ha", "kcal/mol", s.energy)
# Don't use high-energy systems, because these will not likely
# be sampled in MD
if s.energy > high_energy_cutoff:
to_delete.append([composition, j])
continue
# For testing purposes, output
if verbose:
print "Using:", s.name, s.energy
xyz_atoms.append(s.atoms)
system.add(s, len(system.molecules) * system_x_offset)
# Delete the system_names that we aren't actually using due to energy
# being too high
to_delete = sorted(to_delete, key=lambda x: x[1])[::-1]
for d1, d2 in to_delete:
if verbose:
print "Warning - Training Subset %s not included as energy \
is too high..." % systems_by_composition[d1][d2].name
del systems_by_composition[d1][d2]
# Make the box just a little bigger (100) so that we can fit all our
# systems
system.xhi = len(system.molecules) * system_x_offset + 100.0
# Write all of the states we are using to training_sets.xyz
files.write_xyz(xyz_atoms, training_sets_folder + '/' + pickle_file_name)
# Generate our pickle file
print("Saving pickle file %s..." % pfile)
fptr = open(pfile, "wb")
pickle.dump([system, systems_by_composition], fptr)
fptr.close()
# Now we have the data, save it to files for this simulation of
# "run_name" and return parameters
if not os.path.isdir(run_name):
os.mkdir(run_name)
os.chdir(run_name)
mcsmrff_files.write_system_and_training_data(run_name, system,
systems_by_composition)
os.chdir("../")
shutil.copyfile(pfile, "%s/%s.pickle" % (run_name, run_name))
return system, systems_by_composition
else:
low_x = min(x_vals)
high_x = max(x_vals)
for y in yy:
plt.plot(x_vals,y,marker='.',label=str(int(start_val) + i))
if min(y) < low_y: low_y = min(y)
if max(y) > high_y: high_y = max(y)
plt.xlabel(x_label)
plt.ylabel('%s (%s)' % (y_label,u2))
plt.title(title)
if x_range is None: x_range = [low_x, high_x]
if y_range is None: y_range = [low_y, high_y*1.05]
plt.axis([x_range[0], x_range[1], y_range[0], y_range[1]])
plt.legend()
plt.show()
if comp[0] != None: start -= 1
plot(energies,start,x_label,y_label,title,x_range,y_range)
# Write files
files.write_xyz(frames,out_name[:-4])
# Print out values if desired
if p_vals:
for y in energies:
print(str(y))
def pretty_xyz(name,R_MAX=1,F_MAX=50,PROCRUSTS=False,outName=None,write_xyz=False,verbose=False):
#----------
# name = Name of xyz file to read in
# R_MAX = maximum motion per frame
# F_MAX = maximum number of frames allowed
# PROCRUSTES = Center frames or not
# outName = If you wish to output to file, give a name. Defalt name is 'pretty_xyz'
# write_xyz = Write to file. Default False
# Verbose = Outputing what pretty_xyz is doing as it goes
#----------
from copy import deepcopy
# Get data as either frames or a file
if type(name)==type(''): frames = files.read_xyz(name)
elif type(name)==type([]): frames = name
else:
print "Error - Invalid name input. Should be either the name of an xyz file or a list.", sys.exc_info()[0]
exit()
# Loop till we're below R_MAX
while 1:
# Find largest motion_per_frame
if PROCRUSTS: procrustes(frames)
tmp = motion_per_frame(frames)
i = tmp.index(max(tmp))
# Check if we're done
r2 = max(tmp)
if r2 < R_MAX: break
if len(frames) > F_MAX:
print "-------------------------------------------------------"
print tmp
print "-------------------------------------------------------"
print "\n\nError - Could not lower motion below %lg in %d frames." % (R_MAX,F_MAX), sys.exc_info()[0]
exit()
else:
if verbose: print "Currently Frames = %d\tr2 = %lg" % (len(frames),r2)
# Now, split the list, interpolate, and regenerate
if i > 0 and i < len(frames) - 1:
f_low = deepcopy(frames[:i])
f_high = deepcopy(frames[i+1:])
f_mid = interpolate(frames[i-1],frames[i+1],3)
frames = f_low + f_mid + f_high
elif i == 0:
f_low = deepcopy(frames[i])
f_mid = interpolate(frames[i],frames[i+1],3)
f_high = deepcopy(frames[i+1:])
frames = [f_low] + f_mid + f_high
else:
f_low = deepcopy(frames[:i])
f_mid = interpolate(frames[i-1],frames[i],3)
f_high = deepcopy(frames[i])
frames = f_low + f_mid + [f_high]
if verbose: print "\tInterpolated %d,%d ... %lg" % (index-1,index+1,max(motion_per_frame(frames)))
if PROCRUSTS: procrustes(frames)
if write_xyz: files.write_xyz(frames,'pretty_xyz' if outName==None else outName)
else: return frames
def pickle_training_set(run_name,
training_sets_folder="training_set",
pickle_file_name="training_set",
high_energy_cutoff=500.0,
system_x_offset=1000.0,
verbose=False,
extra_parameters={}):
"""
A function to pickle together the training set in a manner that is
readable for MCSMRFF. This is a single LAMMPs data file with each
training set offset alongst the x-axis by system_x_offset. The pickle
file, when read in later, holds a list of two objects. The first is
the entire system as described above. The second is a dictionary of all
molecules in the system, organized by composition.
**Parameters**
run_name: *str*
Name of final training set.
training_sets_folder: *str, optional*
Path to the folder where all the training set data is.
pickle_file_name: *str, optional*
A name for the pickle file and training set system.
high_energy_cutoff: *float, optional*
A cutoff for systems that are too large in energy, as MD is likely
never to sample them.
system_x_offset: *float, optional*
The x offset for the systems to be added by.
verbose: *bool, optional*
Whether to have additional stdout or not.
extra_parameters: *dict, optional*
A dictionaries for additional parameters that do not exist
in the default OPLSAA parameter file.
**Returns**
system: *System*
The entire training set system.
systems_by_composition: *dict, list, Molecule*
Each molecule organized in this hash table.
"""
# Take care of pickle file I/O
if training_sets_folder.endswith("/"):
training_sets_folder = training_sets_folder[:-1]
if pickle_file_name is not None and pickle_file_name.endswith(".pickle"):
pickle_file_name = pickle_file_name.split(".pickle")[0]
pfile = training_sets_folder + "/" + pickle_file_name + ".pickle"
sys_name = pickle_file_name
if os.path.isfile(pfile):
raise Exception("Pickled training set already exists!")
# Generate empty system for your training set
system = None
system = structures.System(box_size=[1e3, 100.0, 100.0], name=sys_name)
systems_by_composition = {}
# For each folder in the training_sets folder lets get the cml file we
# want and write the energies and forces for that file
for name in os.listdir(training_sets_folder):
# We'll read in any training subset that succeeded and print a warning
# on those that failed
try:
result = orca.read("%s/%s/%s.out"
% (training_sets_folder, name, name))
except IOError:
print("Warning - Training Subset %s not included as \
out file not found..." % name)
continue
# Check for convergence
if not result.converged:
print("Warning - Results for %s have not converged." % name)
continue
# Parse the force output and change units. In the case of no force
# found, do not use this set of data
try:
forces = orca.engrad_read("%s/%s/%s.orca.engrad"
% (training_sets_folder, name, name),
pos="Ang")[0]
# Convert force from Ha/Bohr to kcal/mol-Ang
def convert(x):
return units.convert_dist("Ang", "Bohr",
units.convert_energy("Ha",
"kcal",
x)
)
for a, b in zip(result.atoms, forces):
a.fx, a.fy, a.fz = convert(b.fx), convert(b.fy), convert(b.fz)
except (IndexError, IOError):
print("Warning - Training Subset %s not included as \
results not found..." % name)
continue
# Get the bonding information
with_bonds = structures.Molecule("%s/%s/%s.cml"
% (training_sets_folder, name, name),
extra_parameters=extra_parameters,
allow_errors=True,
test_charges=False)
# Copy over the forces read in into the system that has the bonding
# information
for a, b in zip(with_bonds.atoms, result.atoms):
a.fx, a.fy, a.fz = b.fx, b.fy, b.fz
# sanity check on atom positions
if geometry.dist(a, b) > 1e-4:
raise Exception('Atoms are different:', (a.x, a.y, a.z),
(b.x, b.y, b.z)
)
# Rename and save energy
with_bonds.energy = result.energy
with_bonds.name = name
# Now, we read in all the potential three-body interactions that our
# training set takes into account. This will be in a 1D array
composition = ' '.join(sorted([a.element for a in result.atoms]))
if composition not in systems_by_composition:
systems_by_composition[composition] = []
systems_by_composition[composition].append(with_bonds)
# Generate:
# (1) xyz file of various systems as different time steps
# (2) system to simulate
xyz_atoms = []
to_delete = []
for i, composition in enumerate(systems_by_composition):
# Sort so that the lowest energy training subset is first
# in the system
systems_by_composition[composition].sort(key=lambda s: s.energy)
baseline_energy = systems_by_composition[composition][0].energy
# Offset the energies by the lowest energy, and convert energy units
for j, s in enumerate(systems_by_composition[composition]):
s.energy -= baseline_energy
s.energy = units.convert_energy("Ha", "kcal/mol", s.energy)
# Don't use high-energy systems, because these will not likely
# be sampled in MD
if s.energy > high_energy_cutoff:
to_delete.append([composition, j])
continue
# For testing purposes, output
if verbose:
print "Using:", s.name, s.energy
xyz_atoms.append(s.atoms)
system.add(s, len(system.molecules) * system_x_offset)
# Delete the system_names that we aren't actually using due to energy
# being too high
to_delete = sorted(to_delete, key=lambda x: x[1])[::-1]
for d1, d2 in to_delete:
if verbose:
print "Warning - Training Subset %s not included as energy \
is too high..." % systems_by_composition[d1][d2].name
del systems_by_composition[d1][d2]
# Make the box just a little bigger (100) so that we can fit all our
# systems
system.xhi = len(system.molecules) * system_x_offset + 100.0
# Write all of the states we are using to training_sets.xyz
files.write_xyz(xyz_atoms, training_sets_folder + '/' + pickle_file_name)
# Generate our pickle file
print("Saving pickle file %s..." % pfile)
fptr = open(pfile, "wb")
pickle.dump([system, systems_by_composition], fptr)
fptr.close()
# Now we have the data, save it to files for this simulation of
# "run_name" and return parameters
if not os.path.isdir(run_name):
os.mkdir(run_name)
os.chdir(run_name)
mcsmrff_files.write_system_and_training_data(run_name,
system,
systems_by_composition
)
os.chdir("../")
shutil.copyfile(pfile, "%s/%s.pickle" % (run_name, run_name))
return system, systems_by_composition
def job(run_name,
atoms,
ecut,
ecutrho=None,
atom_units="Ang",
route=None,
pseudopotentials=None,
periodic_distance=15,
dumps="dump End Ecomponents ElecDensity",
queue=None,
walltime="00:30:00",
procs=1,
threads=None,
redundancy=False,
previous=None,
mem=2000,
priority=None,
xhost=None,
slurm_allocation=sysconst.slurm_default_allocation):
"""
Wrapper to submitting a JDFTx simulation.
**Parameters**
run_name: *str*
Name of the simulation to be run.
atoms: *list,* :class:`structures.Atom` *, or str*
A list of atoms for the simulation. If a string is passed, it is
assumed to be an xyz file (relative or full path). If None is passed,
then it is assumed that previous was specified.
ecut: *float*
The planewave cutoff energy in Hartree.
ecutrho: *float, optional*
The charge density cutoff in Hartree. By default this is 4 * ecut.
atom_units: *str, optional*
What units your atoms are in. JDFTx expects bohr; however, typically
most work in Angstroms. Whatever units are converted to bohr here.
route: *str, optional*
Any additional script to add to the JDFTx simulation.
pseudopotentials: *list, str, optional*
The pseudopotentials to use in this simulation. If nothing is passed,
a default set of ultra-soft pseudo potentials will be chosen.
periodic_distance: *float, optional*
The periodic box distance in Bohr.
dumps: *str, optional*
The outputs for this simulation.
queue: *str, optional*
What queue to run the simulation on (queueing system dependent).
procs: *int, optional*
How many processors to run the simulation on.
threads: *int, optional*
How many threads to run the simulation on. By default this is procs.
redundancy: *bool, optional*
With redundancy on, if the job is submitted and unique_name is on, then
if another job of the same name is running, a pointer to that job will
instead be returned.
previous: *str, optional*
Name of a previous simulation for which to try reading in
information using the MORead method.
mem: *float, optional*
Amount of memory per processor that is available (in MB).
priority: *int, optional*
Priority of the simulation (queueing system dependent). Priority
ranges (in NBS) from a low of 1 (start running whenever) to a
high of 255 (start running ASAP).
xhost: *list, str or str, optional*
Which processor to run the simulation on(queueing system
dependent).
slurm_allocation: *str, optional*
Whether to use a slurm allocation for this job or not. If so, specify the name.
**Returns**
job: :class:`jobs.Job`
Teturn the job container.
"""
if len(run_name) > 31 and queue is not None:
raise Exception("Job name too long (%d) for NBS. \
Max character length is 31." % len(run_name))
# Generate the orca input file
os.system('mkdir -p jdftx/%s' % run_name)
if previous is not None:
shutil.copyfile("jdftx/%s/%s.xyz" % (previous, previous),
"jdftx/%s/%s.xyz" % (run_name, previous))
shutil.copyfile("jdftx/%s/%s.xyz" % (previous, previous),
"jdftx/%s/%s.xyz" % (run_name, run_name))
shutil.copyfile("jdftx/%s/%s.ionpos" % (previous, previous),
"jdftx/%s/%s.ionpos" % (run_name, previous))
shutil.copyfile("jdftx/%s/%s.lattice" % (previous, previous),
"jdftx/%s/%s.lattice" % (run_name, previous))
os.chdir('jdftx/%s' % run_name)
# Start from a blank output file
if os.path.isfile("%s.out" % run_name):
os.system("mv %s.out %s_prev.out" % (run_name, run_name))
if threads is None:
threads = procs
path_jdftx = sysconst.jdftx_path
if path_jdftx.endswith("/"):
path_jdftx = path_jdftx[:-1]
path_jdftx_scripts = sysconst.jdftx_script_path
if path_jdftx_scripts.endswith("/"):
path_jdftx_scripts = path_jdftx_scripts[:-1]
if atoms is not None:
if not isinstance(atoms, str):
for a in atoms:
a.element = units.elem_i2s(a.element)
files.write_xyz(atoms, "%s.xyz" % run_name)
else:
atoms = files.read_xyz(atoms)
for a in atoms:
a.element = units.elem_i2s(a.element)
files.write_xyz(atoms, "%s.xyz" % run_name)
if run_name.endswith(".xyz"):
run_name = run_name.split(".xyz")[0]
# NOTE! xyzToIonposOpt will convert xyz Angstroms to Bohr
os.system("%s/xyzToIonposOpt %s.xyz %d > xyzToIonpos.log" %
(path_jdftx_scripts, run_name, periodic_distance))
previous_name = None
if previous:
previous_name = previous
previous = "initial-state %s.$VAR" % previous
# First, read in the xyz file to determine unique elements
if pseudopotentials is None and atoms is not None:
pseudopotentials = []
elements = geometry.reduce_list([a.element.lower() for a in atoms])
all_pps = [
fname
for fname in os.listdir("%s/pseudopotentials/GBRV" % path_jdftx)
if fname.endswith("uspp") and "pbe" in fname
]
for e in elements:
potential_pps = []
for pp in all_pps:
if pp.startswith("%s_" % e):
potential_pps.append(pp)
if len(potential_pps) < 1:
raise Exception(
"Unable to automatically grab potential for element %s." %
e)
else:
potential_pps.sort(
) # In theory this should be the "largest" number based on the naming convention.
pseudopotentials.append("GBRV/" + potential_pps[0])
if atoms is None:
pseudopotentials = '''ion-species GBRV/$ID_pbe_v1.2.uspp
ion-species GBRV/$ID_pbe_v1.01.uspp
ion-species GBRV/$ID_pbe_v1.uspp'''
else:
pseudopotentials = "\n".join(
["ion-species %s" % pp for pp in pseudopotentials])
script = '''
# --------------- Molecular Structure ----------------
$$ATOMS$$
coords-type cartesian
$$PREVIOUS$$
# --------------- System Parameters ----------------
elec-cutoff $$ECUT$$ $$ECUTRHO$$
# Specify the pseudopotentials (this defines species O and H):
$$PSEUDOPOTENTIALS$$
# --------------- Outputs ----------------
dump-name $$NAME$$.$VAR #Filename pattern for outputs
$$DUMPS$$ #Output energy components and electron density at the end
'''
atom_str = '''include $$NAME$$.lattice
include $$NAME$$.ionpos'''
if atoms is not None:
atom_str = atom_str.replace("$$NAME$$", run_name)
else:
if previous_name is None:
raise Exception("Forgot to specify previous when atoms is None!")
atom_str = atom_str.replace("$$NAME$$", previous_name)
script = script.replace("$$ATOMS$$", atom_str)
while "$$NAME$$" in script:
script = script.replace("$$NAME$$", run_name)
if ecutrho is None:
ecutrho = ""
while "$$ECUTRHO$$" in script:
script = script.replace("$$ECUTRHO$$", str(ecutrho))
while "$$ECUT$$" in script:
script = script.replace("$$ECUT$$", str(ecut))
while "$$PSEUDOPOTENTIALS$$" in script:
script = script.replace("$$PSEUDOPOTENTIALS$$", pseudopotentials)
if previous is not None:
script = script.replace("$$PREVIOUS$$", previous)
else:
script = script.replace("$$PREVIOUS$$", "")
script = script.replace("$$DUMPS$$", dumps)
if route is not None:
script += "\n# --------------- Outputs ----------------\n\n"
script += route.strip() + "\n\n"
fptr = open("%s.in" % run_name, 'w')
fptr.write(script)
fptr.close()
# Run the simulation
if queue is None:
process_handle = subprocess.Popen("%s/jdftx -i %s.in -o %s.out" %
(path_jdftx, run_name, run_name),
shell=True)
elif queue == 'debug':
print 'Would run', run_name
else:
job_to_submit = "source ~/.zshrc\nmpirun -n %d jdftx -c %d -i %s.in -o %s.out" % (
procs, threads, run_name, run_name)
jobs.submit_job(run_name,
job_to_submit,
procs=procs,
queue=queue,
mem=mem,
priority=priority,
walltime=walltime,
xhosts=xhost,
redundancy=redundancy,
unique_name=True,
slurm_allocation=slurm_allocation)
time.sleep(0.5)
# Copy run script
fname = sys.argv[0]
if '/' in fname:
fname = fname.split('/')[-1]
try:
shutil.copyfile('../../%s' % fname, fname)
except IOError:
# Submitted a job oddly enough that sys.argv[0]
# is not the original python file name, so don't do this
pass
# Return to the appropriate directory
os.chdir('../..')
if queue is None:
return jobs.Job(run_name, process_handle=process_handle)
else:
return jobs.Job(run_name)
def get_pdf(frames,
start=0.0,
stop=5.0,
step=0.1,
cutoff=10.0,
rho=1.0,
quanta=0.001,
output=None,
persist=False):
"""
Obtain the pair distribution function of a list of atoms using the Debyer code.
**Parameters**
frames: *str or list,* :class:`structures.Atom`
An xyz file name (with or without the .xyz extension) or an input frame to calculate the pdf for.
start: *float, optional*
The starting radial distance in Angstroms for the calculated pattern.
stop: *float, optional*
The ending radial distance in Angstroms for the calculated pattern.
step: *float, optional*
Step in Angstroms for the calculated pattern.
cutoff: *float, optional*
Cutoff distance in Angstroms for Interatomic Distance (ID) calculations.
rho: *float, optional*
Numeric density of the system.
quanta: *float, optional*
Interatomic Distance (ID) discritization quanta.
output: *str, optional*
Output file name with NO extension given
persist: *bool, optional*
Whether to persist made .g and .xyz files (True), or remove them (False)
**Returns**
pdf: *list, tuple, float*
A list of tuples holding the pdf data (distance in Angstroms and Intensity).
**References**
* https://debyer.readthedocs.io/en/latest/
"""
# If passed frames and not an xyz file name, write to xyz
append = str(int(random.random() * 1E12))
if type(frames) is not str:
files.write_xyz(frames, "tmp_for_pdf_%s" % append)
file_name = "tmp_for_pdf_%s" % append
else:
file_name = frames
# Else, we want to ensure file_name is correct
if file_name.endswith(".xyz"):
file_name = file_name.split(".xyz")[0]
if output is None:
output = file_name
if stop > cutoff:
raise Exception(
"Stopping position should be larger less than or equal to the cutoff."
)
# Make command for debyer
cmd = "debyer --cutoff=%.2f --quanta=%.2f -g -f%.2f -t%.2f -s%.2f --ro=%.2f -o %s.g %s.xyz" % (
cutoff, quanta, start, stop, step, rho, output, file_name)
# Run debyer and read in the pdf
os.system(cmd)
fptr_pdf = open("%s.g" % output, 'r').read().split("\n")
i = 0
while fptr_pdf[i].strip().startswith("#"):
i += 1
j = len(fptr_pdf) - 1
while fptr_pdf[j].strip() == "":
j -= 1
fptr_pdf = fptr_pdf[i:j + 1]
pdf = [(float(a.split()[0]), float(a.split()[1])) for a in fptr_pdf]
if not persist:
os.system("rm %s.g" % output)
os.system("rm %s.xyz" % file_name)
return pdf
def calculate(coords):
coord_count = 0
for s in NEB.states[1:-1]:
for a in s:
a.x, a.y, a.z = coords[coord_count], coords[
coord_count + 1], coords[coord_count + 2]
coord_count += 3
#start DFT jobs
running_jobs = []
for i, state in enumerate(NEB.states[1:-1]):
guess = '' if NEB.step == 0 else ' Guess=Read'
running_jobs.append(
job('%s-%d-%d' % (NEB.name, NEB.step, i),
NEB.theory + ' Force' + guess,
state,
queue=queue,
force=True,
previous=('%s-%d-%d' % (NEB.name, NEB.step - 1, i))
if NEB.step > 0 else None,
extra_section=extra_section))
#wait for jobs to finish
for j in running_jobs:
j.wait()
#get forces and energies from DFT calculations
energies = []
for i, state in enumerate(NEB.states[1:-1]):
try:
new_energy, new_atoms = parse_atoms(
'%s-%d-%d' % (NEB.name, NEB.step, i))
except:
print 'Job failed: %s-%d-%d' % (NEB.name, NEB.step, i)
exit()
energies.append(new_energy)
for a, b in zip(state, new_atoms):
a.fx = b.fx
a.fy = b.fy
a.fz = b.fz
dft_energies = copy.deepcopy(energies)
#add spring forces to atoms
for i, state in enumerate(NEB.states[1:-1]):
for j, b in enumerate(state):
a, c = NEB.states[i - 1][j], NEB.states[i + 1][j]
if j in spring_atoms:
b.fx += NEB.k * (a.x - b.x) + NEB.k * (c.x - b.x)
b.fy += NEB.k * (a.y - b.y) + NEB.k * (c.y - b.y)
b.fz += NEB.k * (a.z - b.z) + NEB.k * (c.z - b.z)
energies[i] += 0.5 * NEB.k * (utils.dist_squared(
a, b) + utils.dist_squared(b, c))
#set error
NEB.error = sum(energies)
#set forces
NEB.forces = []
for state in NEB.states[1:-1]:
for a in state:
NEB.forces += [-a.fx, -a.fy,
-a.fz] #derivative of the error
#increment step
NEB.step += 1
#write to xyz file
NEB.xyz = open(name + '.xyz', 'w')
for state in NEB.states:
files.write_xyz(state, NEB.xyz)
NEB.xyz.close()
#print data
print NEB.step, NEB.error, (
'%10.7g ' * len(dft_energies)) % tuple(dft_energies)
else:
tail = 'Job has not converged.'
length = max([len(tmp) for tmp in head.split('\n')] +
[len(tmp) for tmp in body.split('\n')] +
[len(tmp) for tmp in tail.split('\n')])
dash = '\n' + ''.join(['-'] * length) + '\n'
if body != '':
print(dash + head + dash + body + dash + tail + dash)
else:
print(dash + head + dash + tail + dash)
try:
if len(data.frames) > 0:
if me:
me = '/fs/home/%s/' % USERNAME
else:
me = ''
files.write_xyz(data.frames, me + out_name[:-4])
if vmd:
os.system('"' + sysconst.vmd_path + '" ' + me + out_name)
elif ovito:
os.system('"' + sysconst.ovito_path + '" ' + me + out_name)
except TypeError:
print("No atomic coordinates available yet...")
except:
print("An unexpected error has occurred.")
sys.exit()
for frame in frames_to_plot:
if frame == 0:
energy = first_E
atoms = first_frame
elif frame == max_frame:
energy = last_E
atoms = last_frame
else:
energy = read("%s-%d-%d" % (run_name,iteration,frame)).energies[-1]
atoms = read("%s-%d-%d" % (run_name,iteration,frame)).atoms
energies.append(units.convert_energy(u1,u2,energy-first_E) * scale)
pathway.append(atoms)
full_energy_list.append(energies)
# Save the final iteration xyz
files.write_xyz(pathway, "%s" % out_name)
# Plot the graph
plot(full_energy_list,iterations_to_plot[0],x_label,y_label,title,x_range,y_range, x_low=frames_to_plot[0])
else:
start = int(sys.argv[2])
stop = int(sys.argv[3])
if '-dft' in sys.argv:
dft = sys.argv[sys.argv.index('-dft') + 1].lower()
if dft not in dft_list:
print("Error - %s not recognized for dft." % dft)
sys.exit()
if [s for s in ['-units','-u'] if s in sys.argv]:
def ovito_xyz_to_gif(frames,
scratch,
fname="image",
camera_pos=(10, 0, 0),
camera_dir=(-1, 0, 0),
size=(800, 600),
delay=10,
display_cell=False,
renderer="OpenGLRenderer",
renderer_settings={},
overwrite=False):
"""
This function will, using the ovito python api, generate either a single
image or a gif of the input frames. Note, a gif is only generated when
more than one frame exists.
**Parameters**
frames: *str* or *list,* :class:`structures.Atom`
A list of frames you wish to generate an image for, or a path to
an xyz file.
scratch: *str*
A directory you want to have each image saved to.
fname: *str, optional*
The prefix for the image names.
camera_pos: *tuple, float, optional*
A tuple of x, y, and z coordinates for the camera to be positioned.
camera_dir: *tuple, float, optional*
The direction the camera is facing.
size: *tuple, int, optional*
Image size (width, height).
delay: *int, optional*
In the event of a gif, how long it should play for.
display_cell: *bool, optional*
Whether to display the box around the system or not.
renderer: *str, optional*
What kind of renderer you wish to use: OpenGL or Tachyon.
renderer_settings: *dict, optional*
Here you can change specific renderer settings.
overwrite: *bool, optional*
Whether to delete any files already existing in the scratch dir.
**Returns**
None
"""
# First ensure we have frames and things in the correct format
if isinstance(frames, str):
frames = open(frames)
if not isinstance(frames[0], list):
frames = [frames]
if not scratch.endswith("/"):
scratch += "/"
# Next, ensure scratch exists
if not os.path.exists(scratch):
os.system("mkdir -p %s" % scratch)
elif len(os.listdir(scratch)) > 0:
if overwrite:
os.system("rm %s/*.png" % scratch)
else:
raise Exception("Error - Scratch directory is not empty!")
# For each frame, generate an image
for i, frame in enumerate(frames):
files.write_xyz(frame, "tmp.xyz")
ovito_xyz_to_image("tmp.xyz",
scratch,
fname="%04d" % i,
camera_pos=camera_pos,
camera_dir=camera_dir,
size=size,
renderer=renderer,
renderer_settings=renderer_settings,
display_cell=display_cell)
os.system("rm tmp.xyz")
# If more than one frame exists, compile to gif
if len(frames) > 1:
cmd = "convert -delay $DELAY -loop 0 $(ls -v $PATH/*.png) output.gif"
holders = [("$PATH", str(scratch)), ("$DELAY", str(delay))]
for s_id, val in holders:
cmd = cmd.replace(s_id, val)
os.system(cmd)
def get_training_set(run_name, use_pickle=True, pickle_file_name=None):
# Take care of pickle file I/O
# Get file name
if pickle_file_name is None:
pfile = "training_sets/training_set.pickle"
else:
pfile = pickle_file_name
system = None
# If the pickle file does not exist, then make it
# If use_pickle is False, then make the read in the data from the
# training_sets folder
if not os.path.isfile(pfile) or not use_pickle:
if pickle_file_name is not None:
raise Exception("Requested file %s, but unable to read it in." %
pickle_file_name)
# Generate the pickle itself if it doesn't exist
# Create the size of the box to be 1000 x 100 x 100 to hold your
# training sets
system = structures.System(box_size=[1e3, 100.0, 100.0],
name="training_set")
systems_by_composition = {}
# For each folder in the training_sets folder lets get the cml file
# we want and write the energies and forces for that file
for name in os.listdir("training_sets"):
# We'll read in any training subset that succeeded and print
# a warning on those that failed
try:
result = orca.read("training_sets/%s/%s.out" % (name, name))
except IOError:
print(
"Warning - Training Subset %s not included as results \
not found..." % name)
continue
# Parse the force output and change units. In the case of no force
# found, do not use this set of data
try:
forces = orca.engrad_read("training_sets/%s/%s.orca.engrad" %
(name, name),
pos="Ang")[0]
# Convert force from Ha/Bohr to kcal/mol-Ang
def convert(x):
units.convert_dist("Ang", "Bohr",
units.convert_energy("Ha", "kcal", x))
for a, b in zip(result.atoms, forces):
a.fx = convert(b.fx)
a.fy = convert(b.fy)
a.fz = convert(b.fz)
except (IndexError, IOError):
print(
"Warning - Training Subset %s not included as results \
not found..." % name)
continue
# Get the bonding information
with_bonds = structures.Molecule("training_sets/%s/system.cml" %
name,
extra_parameters=extra_Pb,
test_charges=False)
# Copy over the forces read in into the system that has the
# bonding information
for a, b in zip(with_bonds.atoms, result.atoms):
a.fx, a.fy, a.fz = b.fx, b.fy, b.fz
if geometry.dist(a, b) > 1e-4:
# sanity check on atom positions
raise Exception('Atoms are different:', (a.x, a.y, a.z),
(b.x, b.y, b.z))
# Rename some things
with_bonds.energy = result.energy
with_bonds.name = name
# Now, we read in all the potential three-body interactions that
# our training set takes into account
# This will be in a 1D array
composition = ' '.join(sorted([a.element for a in result.atoms]))
if composition not in systems_by_composition:
systems_by_composition[composition] = []
systems_by_composition[composition].append(with_bonds)
# Generate (1) xyz file of various systems as different time steps and
# (2) system to simulate
xyz_atoms = []
to_delete = []
for i, composition in enumerate(systems_by_composition):
# Sort so that the lowest energy training subset is first in
# the system
systems_by_composition[composition].sort(key=lambda s: s.energy)
baseline_energy = systems_by_composition[composition][0].energy
# Offset the energies by the lowest energy, convert units of
# the energy
for j, s in enumerate(systems_by_composition[composition]):
s.energy -= baseline_energy
s.energy = units.convert_energy("Ha", "kcal/mol", s.energy)
# Don't use high-energy systems, because these will not likely
# be sampled in MD
if s.energy > 500.0:
to_delete.append([composition, j])
continue
# For testing purposes, output
print "DEBUG:", s.name, s.energy
xyz_atoms.append(s.atoms)
system.add(s, len(system.molecules) * 1000.0)
# Delete the system_names that we aren't actually using due to
# energy being too high
to_delete = sorted(to_delete, key=lambda x: x[1])[::-1]
for d1, d2 in to_delete:
print "Warning - Training Subset %s not included as energy is too \
high..." % systems_by_composition[d1][d2].name
del systems_by_composition[d1][d2]
# Make the box just a little bigger (100) so that we can fit all
# our systems
system.xhi = len(system.molecules) * 1000.0 + 100.0
# Write all of the states we are using to training_sets.xyz
if not os.path.isdir("training_sets"):
os.mkdir("training_sets")
os.chdir("training_sets")
files.write_xyz(xyz_atoms, 'training_sets')
os.chdir("../")
# Generate our pickle file if desired
if use_pickle:
print("Saving pickle file %s..." % pfile)
fptr = open(pfile, "wb")
pickle.dump([system, systems_by_composition], fptr)
fptr.close()
# If use_pickle is true AND the pickle file exists, then we can just
# read it in
if system is None and use_pickle:
print("Reading pickle file %s..." % pfile)
fptr = open(pfile, "rb")
system, systems_by_composition = pickle.load(fptr)
system.name = run_name
fptr.close()
elif system is None:
raise Exception("Requested file %s, but unable to read it in." % pfile)
# Now we have the data, save it to files for this simulation of "run_name"
# and return parameters
if not os.path.isdir("lammps"):
os.mkdir("lammps")
if not os.path.isdir("lammps/%s" % run_name):
os.mkdir("lammps/%s" % run_name)
os.chdir("lammps/%s" % run_name)
mcsmrff_files.write_system_and_training_data(run_name, system,
systems_by_composition)
os.chdir("../../")
return system, systems_by_composition
def get_training_set(run_name, use_pickle=True, pickle_file_name=None):
# Take care of pickle file I/O
# Get file name
if pickle_file_name is None:
pfile = "training_sets/training_set.pickle"
else:
pfile = pickle_file_name
system = None
# If the pickle file does not exist, then make it
# If use_pickle is False, then make the read in the data from the
# training_sets folder
if not os.path.isfile(pfile) or not use_pickle:
if pickle_file_name is not None:
raise Exception("Requested file %s, but unable to read it in."
% pickle_file_name)
# Generate the pickle itself if it doesn't exist
# Create the size of the box to be 1000 x 100 x 100 to hold your
# training sets
system = structures.System(box_size=[1e3, 100.0, 100.0],
name="training_set")
systems_by_composition = {}
# For each folder in the training_sets folder lets get the cml file
# we want and write the energies and forces for that file
for name in os.listdir("training_sets"):
# We'll read in any training subset that succeeded and print
# a warning on those that failed
try:
result = orca.read("training_sets/%s/%s.out" % (name, name))
except IOError:
print("Warning - Training Subset %s not included as results \
not found..." % name)
continue
# Parse the force output and change units. In the case of no force
# found, do not use this set of data
try:
forces = orca.engrad_read("training_sets/%s/%s.orca.engrad"
% (name, name), pos="Ang")[0]
# Convert force from Ha/Bohr to kcal/mol-Ang
def convert(x):
units.convert_dist("Ang",
"Bohr",
units.convert_energy("Ha",
"kcal",
x))
for a, b in zip(result.atoms, forces):
a.fx = convert(b.fx)
a.fy = convert(b.fy)
a.fz = convert(b.fz)
except (IndexError, IOError):
print("Warning - Training Subset %s not included as results \
not found..." % name)
continue
# Get the bonding information
with_bonds = structures.Molecule(
"training_sets/%s/system.cml" % name,
extra_parameters=extra_Pb,
test_charges=False)
# Copy over the forces read in into the system that has the
# bonding information
for a, b in zip(with_bonds.atoms, result.atoms):
a.fx, a.fy, a.fz = b.fx, b.fy, b.fz
if geometry.dist(a, b) > 1e-4:
# sanity check on atom positions
raise Exception('Atoms are different:',
(a.x, a.y, a.z),
(b.x, b.y, b.z))
# Rename some things
with_bonds.energy = result.energy
with_bonds.name = name
# Now, we read in all the potential three-body interactions that
# our training set takes into account
# This will be in a 1D array
composition = ' '.join(sorted([a.element for a in result.atoms]))
if composition not in systems_by_composition:
systems_by_composition[composition] = []
systems_by_composition[composition].append(with_bonds)
# Generate (1) xyz file of various systems as different time steps and
# (2) system to simulate
xyz_atoms = []
to_delete = []
for i, composition in enumerate(systems_by_composition):
# Sort so that the lowest energy training subset is first in
# the system
systems_by_composition[composition].sort(key=lambda s: s.energy)
baseline_energy = systems_by_composition[composition][0].energy
# Offset the energies by the lowest energy, convert units of
# the energy
for j, s in enumerate(systems_by_composition[composition]):
s.energy -= baseline_energy
s.energy = units.convert_energy("Ha", "kcal/mol", s.energy)
# Don't use high-energy systems, because these will not likely
# be sampled in MD
if s.energy > 500.0:
to_delete.append([composition, j])
continue
# For testing purposes, output
print "DEBUG:", s.name, s.energy
xyz_atoms.append(s.atoms)
system.add(s, len(system.molecules) * 1000.0)
# Delete the system_names that we aren't actually using due to
# energy being too high
to_delete = sorted(to_delete, key=lambda x: x[1])[::-1]
for d1, d2 in to_delete:
print "Warning - Training Subset %s not included as energy is too \
high..." % systems_by_composition[d1][d2].name
del systems_by_composition[d1][d2]
# Make the box just a little bigger (100) so that we can fit all
# our systems
system.xhi = len(system.molecules) * 1000.0 + 100.0
# Write all of the states we are using to training_sets.xyz
if not os.path.isdir("training_sets"):
os.mkdir("training_sets")
os.chdir("training_sets")
files.write_xyz(xyz_atoms, 'training_sets')
os.chdir("../")
# Generate our pickle file if desired
if use_pickle:
print("Saving pickle file %s..." % pfile)
fptr = open(pfile, "wb")
pickle.dump([system, systems_by_composition], fptr)
fptr.close()
# If use_pickle is true AND the pickle file exists, then we can just
# read it in
if system is None and use_pickle:
print("Reading pickle file %s..." % pfile)
fptr = open(pfile, "rb")
system, systems_by_composition = pickle.load(fptr)
system.name = run_name
fptr.close()
elif system is None:
raise Exception("Requested file %s, but unable to read it in." % pfile)
# Now we have the data, save it to files for this simulation of "run_name"
# and return parameters
if not os.path.isdir("lammps"):
os.mkdir("lammps")
if not os.path.isdir("lammps/%s" % run_name):
os.mkdir("lammps/%s" % run_name)
os.chdir("lammps/%s" % run_name)
mcsmrff_files.write_system_and_training_data(run_name,
system,
systems_by_composition)
os.chdir("../../")
return system, systems_by_composition
def pdf_metric(A,
ref=None,
persist=False,
lammps_job=False,
start=0.0,
stop=10.0,
step=0.1,
cutoff=3.0,
quanta=0.001,
disregard=[]):
# If we are checking a lammps job, grab the xyz file from
# lammps/run_name/run_name.xyz
if lammps_job is True:
if A.endswith(".xyz"):
A.split(".xyz")[0]
if A.endswith(".dump"):
A.split(".dump")[0]
if A.endswith(".data"):
A.split(".data")[0]
A = read_dump_PbCl3MA("lammps/%s/%s2.dump" % (A, A))
# If we passed a string, then read in the file
if type(A) is str:
if not A.endswith(".xyz"):
A += ".xyz"
A = files.read_xyz(A)
# Assume reference is the first frame
if ref is None:
ref = A[0]
else:
raise Exception("This is not coded yet.")
B = copy.deepcopy([A[0], A[-1]])
# Remove anything in disregard
for i, frame in enumerate(B):
to_kill = []
for j, atom in enumerate(frame):
if atom.element in disregard:
to_kill.append(j)
to_kill = sorted(to_kill)[::-1]
for k in to_kill:
del B[i][k]
pdf_ref = debyer.get_pdf(B[0],
persist=persist,
output="tmp_pdf_ref",
start=start,
stop=stop,
step=step,
cutoff=cutoff,
quanta=quanta)
pdf_final = debyer.get_pdf(B[-1],
persist=persist,
output="tmp_pdf_final",
start=start,
stop=stop,
step=step,
cutoff=cutoff,
quanta=quanta)
files.write_xyz([B[0], B[-1]], "pdf_metric_debug")
# Split lists
pdf_ref = zip(*pdf_ref)
pdf_final = zip(*pdf_final)
difference = [(a - b)**2 for a, b in zip(pdf_ref[1], pdf_final[1])]
rms = (np.asarray(difference).sum() / float(len(difference)))**0.5
if not persist:
os.system("rm pdf_metric_debug.xyz")
return rms, [pdf_ref, pdf_final]
def pdf_metric(A, ref=None, persist=False, lammps_job=False, start=0.0,
stop=10.0, step=0.1, cutoff=3.0, quanta=0.001, disregard=[]):
# If we are checking a lammps job, grab the xyz file from
# lammps/run_name/run_name.xyz
if lammps_job is True:
if A.endswith(".xyz"):
A.split(".xyz")[0]
if A.endswith(".dump"):
A.split(".dump")[0]
if A.endswith(".data"):
A.split(".data")[0]
A = read_dump_PbCl3MA("lammps/%s/%s2.dump" % (A, A))
# If we passed a string, then read in the file
if type(A) is str:
if not A.endswith(".xyz"):
A += ".xyz"
A = files.read_xyz(A)
# Assume reference is the first frame
if ref is None:
ref = A[0]
else:
raise Exception("This is not coded yet.")
B = copy.deepcopy([A[0], A[-1]])
# Remove anything in disregard
for i, frame in enumerate(B):
to_kill = []
for j, atom in enumerate(frame):
if atom.element in disregard:
to_kill.append(j)
to_kill = sorted(to_kill)[::-1]
for k in to_kill:
del B[i][k]
pdf_ref = debyer.get_pdf(B[0],
persist=persist,
output="tmp_pdf_ref",
start=start,
stop=stop,
step=step,
cutoff=cutoff,
quanta=quanta)
pdf_final = debyer.get_pdf(B[-1],
persist=persist,
output="tmp_pdf_final",
start=start,
stop=stop,
step=step,
cutoff=cutoff,
quanta=quanta)
files.write_xyz([B[0], B[-1]], "pdf_metric_debug")
# Split lists
pdf_ref = zip(*pdf_ref)
pdf_final = zip(*pdf_final)
difference = [(a - b)**2 for a, b in zip(pdf_ref[1], pdf_final[1])]
rms = (np.asarray(difference).sum() / float(len(difference)))**0.5
if not persist:
os.system("rm pdf_metric_debug.xyz")
return rms, [pdf_ref, pdf_final]
body += '\t'.join([str(s) for s in line])+'\n'
body = utils.spaced_print(body, delim='\t')
if data.converged:
tail = 'Job converged in %.2e seconds' % data.time
else:
tail = 'Job has not converged.'
length = max([len(tmp) for tmp in head.split('\n')] + [len(tmp) for tmp in body.split('\n')] + [len(tmp) for tmp in tail.split('\n')])
dash = '\n'+''.join(['-']*length)+'\n'
if body != '':
print(dash+head+dash+body+dash+tail+dash)
else:
print(dash+head+dash+tail+dash)
try:
if len(data.frames) > 0:
if me:
me = '/fs/home/%s/' % USERNAME
else:
me = ''
files.write_xyz(data.frames,me + out_name[:-4])
if vmd:
os.system('"'+sysconst.vmd_path + '" ' + me + out_name)
except TypeError:
print("No atomic coordinates available yet...")
except:
print("An unexpected error has occurred.")
sys.exit()
energy = first_E
atoms = first_frame
elif frame == max_frame:
energy = last_E
atoms = last_frame
else:
energy = read("%s-%d-%d" %
(run_name, iteration, frame)).energies[-1]
atoms = read("%s-%d-%d" % (run_name, iteration, frame)).atoms
energies.append(
units.convert_energy(u1, u2, energy - first_E) * scale)
pathway.append(atoms)
full_energy_list.append(energies)
# Save the final iteration xyz
files.write_xyz(pathway, "%s" % out_name)
# Plot the graph
plot(full_energy_list,
iterations_to_plot[0],
x_label,
y_label,
title,
x_range,
y_range,
x_low=frames_to_plot[0])
else:
start = int(sys.argv[2])
stop = int(sys.argv[3])
