def load_mols(dataset, path):
if os.path.exists(path+"val_mol.xyz"):
val_mols = aio.iread(path+"val_mol.xyz")
pre_mols = aio.iread(path+"pre_mol.xyz")
return list(val_mols), list(pre_mols)
else:
dataset = choose_set(dataset)
net = Net(8)
best_model = torch.load(path+"best_model.pth")
net.load_state_dict(best_model["Model"])
#Using CUDA if available
device = torch.device("cuda:0" if torch.cuda.is_available()\
else "cpu")
print("Compute device: ")
print(device)
net.to(device)
torch.manual_seed(42)
train_set, test_set = torch.utils.data.random_split(dataset,
[len(dataset)-167, 167])
torch.manual_seed(np.random.randint(100000))
loader = torch.utils.data.DataLoader(test_set,
batch_size=1,
num_workers=0,
shuffle=False,
collate_fn=collate_none)
val_mols = []
pre_mols = []
for item in loader:
try:
tensors, atoms = item
inputs, targets = tensors
except TypeError:
print("Molecule is too big")
continue
# Moving the tensors to device
inputs, targets = inputs.to(device).float(),\
targets.to(device).long()
outputs = net(inputs)
pre_mols.append(atoms_reconstruction(outputs))
val_mols.append(atoms[0])
aio.write(path+"val_mol.xyz", val_mols)
aio.write(path+"pre_mol.xyz", pre_mols)
return val_mols, pre_mols
def plot(self, out_folder='.', unit_cell='POSCAR', code_name='vasp'):
try:
from ase.io.trajectory import Trajectory
from ase.io import read, iread
from ase.visualize import view
except ImportError:
raise ImportError(
"\nThe parent directory of ase package must be included in 'sys.path'"
)
if code_name == 'espresso':
code_name = 'espresso-in' # aims, espresso-in, vasp
atom = read(unit_cell, format=code_name)
_current_position_true = atom.positions.copy()[
self.process.unit_cell.atom_true]
_mass_weight = self.process.unit_cell.mass_true.reshape(
(-1, 3)) / self.process.unit_cell.mass_true.max()
for mode_ind in self.mode_inds:
traj = Trajectory(
out_folder + "/Trajectory_{0}.traj".format(mode_ind), 'w')
for ind, x in enumerate(
np.linspace(0, 2 * np.pi, self.num_images, endpoint=False),
1):
atom.positions[self.process.unit_cell.atom_true] = _current_position_true \
+ np.sin(x) * self.mode[mode_ind, :].reshape((-1, 3)).real / np.sqrt(_mass_weight)
traj.write(atom)
traj.close()
atoms = iread(out_folder + "/Trajectory_{0}.traj".format(mode_ind))
view(atoms)
def test_dlp3():
cells = []
cells.append(
np.array([[23.9999973028, 0.0, 0.0], [0.0, 23.9999973028, 0.0],
[0.0, 0.0, 23.9999973028]]))
cells.append(
np.array([[23.9999947494, 0.0, 0.0], [0.0, 23.9999947494, 0.0],
[0.0, 0.0, 23.9999947494]]))
cells.append(
np.array([[23.9999911871, 0.0, 0.0], [0.0, 23.9999911871, 0.0],
[0.0, 0.0, 23.9999911871]]))
traj = aseIO.read(fd3, format='dlp-history', index=slice(0, None))
assert len(traj) == 3
traj = aseIO.iread(fd3, format='dlp-history', index=slice(0, None))
for i, frame in enumerate(traj):
assert len(frame) == 4
assert all(frame.symbols == 'OHHX')
assert np.isclose(frame.get_cell(), cells[i]).all()
symbols = frame.get_chemical_symbols()
traj = iread_dlp_history(fd3, symbols)
for i, frame in enumerate(traj):
assert len(frame) == 4
assert all(frame.symbols == 'OHHX')
assert np.isclose(frame.get_cell(), cells[i]).all()
assert frame.has('initial_charges')
def test_dlp():
cells = []
cells.append(
np.array([[23.01, -0.3943E-01, 0.4612E-01],
[-0.9486E-01, 22.98, 0.4551], [0.6568, 0.7694, 19.21]]))
cells.append(
np.array([[22.90, -0.3925E-01, 0.4591E-01],
[-0.9443E-01, 22.88, 0.4531], [0.6538, 0.7660, 19.12]]))
cells.append(
np.array([[22.73, -0.3896E-01, 0.4557E-01],
[-0.9374E-01, 22.71, 0.4497], [0.6490, 0.7603, 18.98]]))
traj = aseIO.read(fd, format='dlp-history', index=slice(0, None))
assert len(traj) == 3
traj = aseIO.iread(fd, format='dlp-history', index=slice(0, None))
for i, frame in enumerate(traj):
assert len(frame) == 2
assert all(frame.symbols == 'ONi')
assert np.isclose(frame.get_cell(), cells[i]).all()
symbols = frame.get_chemical_symbols()
traj = iread_dlp_history(fd, symbols)
for i, frame in enumerate(traj):
assert len(frame) == 2
assert all(frame.symbols == 'ONi')
assert np.isclose(frame.get_cell(), cells[i]).all()
def test_vasp_out(outcar):
tol = 1e-6
a1 = read(outcar, index=-1)
assert isinstance(a1, Atoms)
assert np.isclose(a1.get_potential_energy(force_consistent=True),
-68.22868532,
atol=tol)
assert np.isclose(a1.get_potential_energy(force_consistent=False),
-68.23102426,
atol=tol)
a2 = read(outcar, index=':')
assert isinstance(a2, list)
assert isinstance(a2[0], Atoms)
assert len(a2) == 1
gen = iread(outcar, index=':')
assert inspect.isgenerator(gen)
for fc in (True, False):
for a3 in gen:
assert isinstance(a3, Atoms)
assert np.isclose(a3.get_potential_energy(force_consistent=fc),
a1.get_potential_energy(force_consistent=fc),
atol=tol)
def parse_loose_molecules(self, directory, energy_file):
"""
Args:
directory (str): Root directory of filetree of
crystal/molecule data.
energy_file: Dictionary of filename-value pairs.
"""
libver = self.settings['libver']
index = self.settings['index']
form = self.settings['input_format']
if form == '':
form = None
if os.path.isfile(self.output_name):
self.combine_collated(self.output_name, add_to_output=False)
else:
self.entries = []
self.sources = []
self.m_count = 0
m_count_start = int(self.m_count)
with open(energy_file, 'r') as energy_handle:
energy_text = energy_handle.read()
energy_data = find_energy_in_text(energy_text, loose=True)
with h5py.File(self.output_name, 'a', libver=libver) as h5f:
for root, dirs, files in os.walk(directory):
sorted_files = sorted(files)
for filename in sorted_files[slice_from_str(index)]:
try:
molecules = iread(os.path.join(root, filename),
format=form)
except (ValueError, IOError, IndexError):
continue
for molecule in molecules:
m_name = '{}.{}'.format(filename, self.m_count)
try:
path = 'molecules/{}'.format(m_name)
energy_val = energy_data[filename]
mol = CollatedMolecule()
mol.from_ase(molecule, self.sys_elements,
self.element_order)
mol.set_energy(energy_val)
entry = mol.to_file(h5f, path)
self.entries.append(entry)
self.sources.append(filename)
self.m_count += 1
print('read',
self.m_count - m_count_start,
end='\r')
except KeyError:
print('no corresponding energy_data:', m_name)
continue
except AssertionError:
print('error in', m_name, end='\n\n')
traceback.print_exc()
continue
self.update_system_details(h5f)
def test_nomad(datadir):
from ase.io import iread
path = datadir / 'nomad-images.nomad-json'
images = list(iread(path))
assert len(images) == 3
for atoms in images:
assert all(atoms.pbc)
assert (atoms.cell > 0).sum() == 3
assert atoms.get_chemical_formula() == 'As24Sr32'
def main():
if not shutil.which("cp2k"):
raise unittest.SkipTest('cp2k command not available')
inp = """&MOTION
&PRINT
&TRAJECTORY SILENT
FORMAT DCD_ALIGNED_CELL
&END TRAJECTORY
&END PRINT
&MD
STEPS 5
&END MD
&END MOTION
&GLOBAL
RUN_TYPE MD
&END GLOBAL"""
calc = CP2K(label='test_dcd', max_scf=1, inp=inp)
h2 = molecule('H2', calculator=calc)
h2.center(vacuum=2.0)
h2.set_cell([10.0, 10.0, 10.0, 90.0, 90.0, 90.0])
h2.set_pbc(True)
energy = h2.get_potential_energy()
assert not energy == None
subprocess.call(['cp2k', '-i', 'test_dcd.inp', '-o', 'test_dcd.out'])
h2_end = io.read('test_dcd-pos-1.dcd')
assert (h2_end.symbols == 'X').all()
traj = io.read('test_dcd-pos-1.dcd',
ref_atoms=h2,
index=slice(0, None),
aligned=True)
ioITraj = io.iread('test_dcd-pos-1.dcd',
ref_atoms=h2,
index=slice(0, None),
aligned=True)
with open('test_dcd-pos-1.dcd', 'rb') as f:
itraj = iread_cp2k_dcd(f,
indices=slice(0, None),
ref_atoms=h2,
aligned=True)
for i, iMol in enumerate(itraj):
ioIMol = next(ioITraj)
assert compare_atoms(iMol, traj[i]) == []
assert compare_atoms(iMol, ioIMol) == []
assert iMol.get_pbc().all()
traj = io.read('test_dcd-pos-1.dcd', ref_atoms=h2, index=slice(0, None))
pbc = [mol.get_pbc() for mol in traj]
assert not np.any(pbc)
print('passed test "CP2K_DCD"')
def upload(self, file: Path, extra_infos=None, store_calc=True):
if isinstance(file, str):
file = Path(file)
extra_info = {}
if extra_infos:
for info in extra_infos:
extra_info.update(extras.parser.parse(info))
extra_info['filename'] = str(file)
data = iread(str(file))
self.push(data, extra_info, store_calc=store_calc)
def test_nomad():
fname = 'nmd.test.nomad-json'
with open(fname, 'w') as fd:
fd.write(nomad_data)
from ase.io import iread
images = list(iread(fname))
assert len(images) == 3
for atoms in images:
assert all(atoms.pbc)
assert (atoms.cell > 0).sum() == 3
assert atoms.get_chemical_formula() == 'As24Sr32'
def read(filename, format='auto', log=None, emap=None, top=None, units='real'):
from ase.io import iread
if format == 'auto':
if filename.endswith('.yml'):
format = 'pinn'
elif filename.endswith('.dump'):
format = 'dump'
elif filename.endswith('.trr'):
format = 'trr'
if format == 'pinn':
ds = pinn_reader(filename)
elif format == 'dump':
ds = map(atoms2dict, iread(filename, units=units))
if log is not None:
update_energy = lambda struct, energy: dict(struct, e_data=energy)
energies = read_lammps_log(log, units=units)
ds = map(update_energy, ds, energies)
elif format == 'trr':
ds = read_trr(filename, top)
if log is not None:
update_energy = lambda struct, energy: dict(struct, e_data=energy)
energies = read_gromacs_xvg(log)
ds = map(update_energy, ds, energies)
else:
ds = map(atoms2dict, iread(filename))
if emap is not None:
emap = {
int(s.split(':')[0]): int(s.split(':')[1])
for s in emap.split(',')
}
map_elems = lambda data: dict(data,
elems=[emap[e] for e in data['elems']])
ds = map(map_elems, ds)
return ds
def parse_molecules(self, filename, energy_file):
"""
Args:
filename (str): Filename of crystal/molecule data.
energy_file: File with property values as floats.
"""
libver = self.settings['libver']
index = self.settings['index']
keyword = self.settings['keyword']
form = self.settings['input_format']
if form == '':
form = None
if os.path.isfile(self.output_name):
self.combine_collated(self.output_name, add_to_output=False)
else:
self.entries = []
self.sources = []
self.m_count = 0
m_count_start = int(self.m_count)
with open(energy_file, 'r') as energy_handle:
energy_text = energy_handle.read()
energy_data = find_energy_in_text(energy_text, keyword=keyword)
with h5py.File(self.output_name, 'a', libver=libver) as h5f:
molecules = iread(filename, format=form, index=index)
for molecule in molecules:
m_name = '{}.{}'.format(filename, self.m_count)
try:
path = 'molecules/{}'.format(m_name)
energy_val = energy_data[self.m_count - m_count_start]
mol = CollatedMolecule()
mol.from_ase(molecule, self.sys_elements,
self.element_order)
mol.set_energy(energy_val)
entry = mol.to_file(h5f, path)
self.entries.append(entry)
self.m_count += 1
print('read', self.m_count - m_count_start, end='\r')
except IndexError:
print('no corresponding energy_data:', m_name)
continue
except AssertionError:
print('error in', m_name, end='\n\n')
traceback.print_exc()
continue
self.sources.append(filename)
self.update_system_details(h5f)
def traj_fp_vasp(traj_file, work_path, chemical_symbol=None, index="::"):
"""
Export VASP POSCAR files from trajectory.
:param traj_file:
:param work_path:
:param chemical_symbol:
:param index:
:return:
"""
stcs = iread(os.path.abspath(traj_file), index=index)
os.makedirs(work_path, exist_ok=True)
for i, j in enumerate(stcs):
if chemical_symbol:
j.set_chemical_symbols(chemical_symbol)
task_path = os.path.join(work_path, f'task.{str(i).zfill(6)}')
os.makedirs(task_path, exist_ok=True)
write(os.path.join(task_path, 'POSCAR'), j, vasp5=True)
def plot(self, out_folder='.', unit_cell='POSCAR', code_name='vasp'):
"""
Visualize phonon Mode using modules of the `Atomic Simulation Environment (ASE) <https://wiki.fysik.dtu.dk/ase/index.html>`_.
:param out_folder: Folder path for **Trajectory.traj** to be stored, defaults to .
:type out_folder: str
:param unit_cell: Path of unit cell input file, defaults to POSCAR
:type unit_cell: str
:param code_name: Specification of the file-format by a DFT program, defaults to vasp
:type code_name: str
"""
try:
from ase.io.trajectory import Trajectory
from ase.io import read, iread
from ase.visualize import view
except ImportError:
raise ImportError(
"\nThe parent directory of ase package must be included in 'sys.path'"
)
if code_name == 'espresso':
code_name = 'espresso-in' # aims, espresso-in, vasp
atom = read(unit_cell, format=code_name)
_current_position_true = np.transpose(
atom.positions.copy()[self.process.unit_cell.atom_true, :])
_mass_weight = np.transpose(
self.process.unit_cell.mass_true.reshape(
(-1, 3))) / self.process.unit_cell.mass_true.max()
for mode_ind in self.mode_inds:
traj = Trajectory(
out_folder + "/Trajectory_{0}.traj".format(mode_ind), 'w')
for _, x in enumerate(
np.linspace(0, 2 * np.pi, self.num_images, endpoint=False),
1):
atom.positions[self.process.unit_cell.atom_true, :] = \
np.transpose(_current_position_true + np.sin(x)
* np.transpose(self.mode[mode_ind, :].reshape((-1, 3)).real) / np.sqrt(_mass_weight))
# np.sin(x + np.dot(_q, _current_position_true))
traj.write(atom)
traj.close()
atoms = iread(out_folder + "/Trajectory_{0}.traj".format(mode_ind))
view(atoms)
def test_dcd(factory, factories):
# (Should the cp2k_main executable live on the cp2k factory?)
exes = factories.executables
cp2k_main = exes.get('cp2k_main')
if cp2k_main is None:
pytest.skip('Please define "cp2k_main" in testing executables. '
'It should point to the main cp2k executable '
'(not the shell)')
calc = factory.calc(label='test_dcd', max_scf=1, inp=inp)
h2 = molecule('H2', calculator=calc)
h2.center(vacuum=2.0)
h2.set_pbc(True)
energy = h2.get_potential_energy()
assert energy is not None
subprocess.check_call(
[cp2k_main, '-i', 'test_dcd.inp', '-o', 'test_dcd.out'])
h2_end = io.read('test_dcd-pos-1.dcd')
assert (h2_end.symbols == 'X').all()
traj = io.read('test_dcd-pos-1.dcd',
ref_atoms=h2,
index=slice(0, None),
aligned=True)
ioITraj = io.iread('test_dcd-pos-1.dcd',
ref_atoms=h2,
index=slice(0, None),
aligned=True)
with open('test_dcd-pos-1.dcd', 'rb') as fd:
itraj = iread_cp2k_dcd(fd,
indices=slice(0, None),
ref_atoms=h2,
aligned=True)
for i, iMol in enumerate(itraj):
ioIMol = next(ioITraj)
assert compare_atoms(iMol, traj[i]) == []
assert compare_atoms(iMol, ioIMol) == []
assert iMol.get_pbc().all()
traj = io.read('test_dcd-pos-1.dcd', ref_atoms=h2, index=slice(0, None))
pbc = [mol.get_pbc() for mol in traj]
assert not np.any(pbc)
def test_traj_bytesio():
from ase.io import iread, write
from ase.build import bulk
from ase.collections import g2
import io
images = [bulk('Si') + bulk('Fe')] + list(g2)
buf = io.BytesIO()
write(buf, images, format='traj')
txt = buf.getvalue()
buf = io.BytesIO()
buf.write(txt)
buf.seek(0)
images2 = list(iread(buf, format='traj'))
for atoms1, atoms2 in zip(images, images2):
assert atoms1 == atoms2
def accSuccess(runs=30, directory="runs0", tolerance=5): #Max spaghetti
convergenceIndecies = []
lengths = []
for i in range(0, runs):
try:
structures = iread(directory + "/run" + str(i) +
"/structures.traj")
energies = [
structure.get_total_energy() for structure in structures
]
lengths.append(len(energies))
except:
continue
emin = np.amin(energies)
relativeEnergies = [e - emin for e in energies]
# convergenceEnergy = np.amin(relativeEnergies) #Den er naturligvis 0 for den valgte reference
convergence = np.where(
np.array(relativeEnergies) < tolerance, 1, 0
) #Skal have fundet en bedre reference, men denne virker i tilfælde, hvor problemet er garanteret konvergens. Eventuelt brug Emin fra baseCase som reference i CrapGrænsen.
convergenceIndex = np.nonzero(convergence)
convergenceIndecies.append(convergenceIndex[0][0])
space = np.linspace(0, np.amin(lengths) - 1, np.amin(lengths))
accSucces = []
a = 0
for index in space:
for convindex in convergenceIndecies:
if convindex == int(index):
a += 1
accSucces.append(a / len(convergenceIndecies))
plt.plot(space, accSucces, color="black")
plt.xlabel("Iterations")
plt.ylabel("Accumulated Succes (%)")
plt.show()
# accSuccess()
def test_ioformat(format, atoms, catch_warnings):
if format in ['proteindatabank', 'netcdftrajectory']:
warnings.simplefilter('ignore', UserWarning)
# netCDF4 uses np.bool which may cause warnings in new numpy.
warnings.simplefilter('ignore', DeprecationWarning)
if format == 'dlp4':
atoms.pbc = (1, 1, 0)
images = [atoms, atoms]
io = ioformats[format]
print('{0:20}{1}{2}{3}{4}'.format(format, ' R'[io.can_read],
' W'[io.can_write], '+1'[io.single],
'SF'[io.acceptsfd]))
fname1 = 'io-test.1.{}'.format(format)
fname2 = 'io-test.2.{}'.format(format)
if io.can_write:
write(fname1, atoms, format=format)
if not io.single:
write(fname2, images, format=format)
if io.can_read:
for a in [read(fname1, format=format), read(fname1)]:
check(a, atoms, format)
if not io.single:
if format in ['json', 'db']:
aa = read(fname2, index='id=1') + read(fname2,
index='id=2')
else:
aa = [read(fname2), read(fname2, 0)]
aa += read(fname2, ':')
for a in iread(fname2, format=format):
aa.append(a)
assert len(aa) == 6, aa
for a in aa:
check(a, atoms, format)
def run(args, parser):
files = [os.path.split(path)[1] for path in args.filenames]
lmax = max([len(f) for f in files]) + 2
print('file'.ljust(lmax) +
'type score a b component counts')
print('=' * lmax + '===============================================')
merge = not args.no_merge
# reading CIF files can produce a ton of distracting warnings
with warnings.catch_warnings():
warnings.filterwarnings('ignore')
for path, f in zip(args.filenames, files):
for atoms in iread(path):
result = analyze_dimensionality(atoms, merge=merge)
if not args.full:
result = result[:1]
for i, entry in enumerate(result):
dimtype = entry.dimtype.rjust(4)
score = '{:.3f}'.format(entry.score).ljust(5)
a = '{:.3f}'.format(entry.a).ljust(5)
b = '{:.3f}'.format(entry.b).ljust(5)
if i == 0:
name = f.ljust(lmax)
else:
name = ' ' * lmax
line = ('{}{}' + ' {}' * 4).format(
name, dimtype, score, a, b, entry.h)
print(line)
if args.full:
print()
images = [atoms, atoms]
io = get_ioformat(format)
print('{0:20}{1}{2}{3}{4}'.format(format,
' R'[bool(io.read)],
' W'[bool(io.write)],
'+1'[io.single],
'SF'[io.acceptsfd]))
fname1 = '{}/io-test.1.{}'.format(testdir, format)
fname2 = '{}/io-test.2.{}'.format(testdir, format)
if io.write:
write(fname1, atoms, format=format)
if not io.single:
write(fname2, images, format=format)
if io.read:
for a in [read(fname1, format=format), read(fname1)]:
check(a, atoms, format)
if not io.single:
if format in ['json', 'db']:
aa = read(fname2 + '@id=1') + read(fname2 + '@id=2')
else:
aa = [read(fname2), read(fname2, 0)]
aa += read(fname2, ':')
for a in iread(fname2, format=format):
aa.append(a)
assert len(aa) == 6, aa
for a in aa:
check(a, atoms, format)
def parse_images(data, format=None, **kwargs):
"""Parse string or bytes into list of atoms objects."""
buf = BytesIO(data)
images = list(iread(buf, format=format, **kwargs))
return images
def test(format):
if format in ['abinit', 'castep-cell', 'dftb', 'eon', 'gaussian']:
# Someone should do something ...
return
if format in ['v-sim', 'mustem']:
# Standalone test used as not compatible with 1D periodicity
return
if format in ['mustem']:
# Standalone test used as specific arguments are required
return
if format in ['dmol-arc', 'dmol-car', 'dmol-incoor']:
# We have a standalone dmol test
return
if format in ['gif', 'mp4']:
# Complex dependencies; see animate.py test
return
if format in ['postgresql', 'trj', 'vti', 'vtu']:
# Let's not worry about these.
return
if not matplotlib and format in ['eps', 'png']:
return
if not etree and format == 'exciting':
return
if not Scientific and format == 'etsf':
return
if not netCDF4 and format == 'netcdftrajectory':
return
atoms = get_atoms()
if format == 'dlp4':
atoms.pbc = (1, 1, 0)
images = [atoms, atoms]
io = get_ioformat(format)
print('{0:20}{1}{2}{3}{4}'.format(format, ' R'[bool(io.read)],
' W'[bool(io.write)], '+1'[io.single],
'SF'[io.acceptsfd]))
fname1 = '{}/io-test.1.{}'.format(testdir, format)
fname2 = '{}/io-test.2.{}'.format(testdir, format)
if io.write:
write(fname1, atoms, format=format)
if not io.single:
write(fname2, images, format=format)
if io.read:
for a in [read(fname1, format=format), read(fname1)]:
check(a, atoms, format)
if not io.single:
if format in ['json', 'db']:
aa = read(fname2 + '@id=1') + read(fname2 + '@id=2')
else:
aa = [read(fname2), read(fname2, 0)]
aa += read(fname2, ':')
for a in iread(fname2, format=format):
aa.append(a)
assert len(aa) == 6, aa
for a in aa:
check(a, atoms, format)
def test_iread_path():
path = Path('hello')
iterator = iread(path)
with pytest.raises(FileNotFoundError):
next(iterator)
count = 1
else:
count += 1
sc.append((psym, count))
return sc
args = sys.argv
file = args[1]
tag = args[2]
dir = args[3]
os.makedirs(dir, exist_ok=True)
counter = 1
atoms = iread(file, index=':', format='xyz')
# file for make binary data step
with open("input_tag.dat", "w") as tagfile:
for a in atoms:
forces = a.get_forces()
positions = a.get_positions()
scaled_position = a.get_scaled_positions()
energy = a.get_total_energy()
natom = len(forces)
tagfile.write(tag + str(natom) + "atoms" + "_" + str(counter) + "\n")
xdatcar = dir + '/positions_' + tag + str(natom) + "atoms" + "_" + str(
counter) + ".dat"
with open(xdatcar, "w") as xdat:
symbol_count = _symbol_count_from_symbols(a.get_chemical_symbols())
label = ' '.join([s for s, _ in symbol_count])
if 'Total valence charge' in line:
elm[lst_elm] = float(line.split()[-1])
if 'mulliken: Atomic and Orbital Populations' in line:
in_mulliken = True
charge = []
lo = True
if in_mulliken and 'Species:' in line:
chg_0 = elm[line.split()[-1]]
try:
test = int(line.split()[0])
except ValueError:
lo = False
if in_mulliken and lo:
x = line.split()[:2]
x.append(chg_0)
charge.append(x)
if 'mulliken: Qtot =' in line:
Qtot.append(float(line.split()[-1]))
next(f)
if 'mulliken' not in next(f):
in_mulliken = False
# append for each md steps
total_sets.append(np.array(charge, dtype='float'))
total = np.array(total_sets)
a = iread('siesta.AXSF')
for i, j in zip(a, total[1:]):
i.set_initial_charges(j[:, 1] - j[:, 2])
write('total_charges.xyz', i, append=True)
final.positions[2:5] = initial.positions[[3, 4, 2]]
# Generate blank images.
images = [initial]
for i in range(9):
images.append(initial.copy())
for image in images:
image.calc = EMT()
images.append(final)
neb = NEB(images, climb=True)
neb.interpolate(method='idpp') #idpp插值，设置初猜
# set calculator
for atoms in images:
atoms.calc = EMT()
atoms.get_potential_energy()
# Optimize:
py_fname = os.path.splitext(sys.argv[0])[0]
traj_fname = "{}.traj".format(py_fname)
log_fname = "{}.log".format(py_fname)
optimizer = FIRE(neb, trajectory=traj_fname, logfile=log_fname)
optimizer.run(fmax=0.04)
neb_result = list(iread(traj_fname))
for i in neb_result:
#print(i.get_potential_energy())
pass
neb_result = NEBTools(neb_result)
neb_result.plot_bands(True, True, label=py_fname)
print(neb_result.get_barrier(), neb_result.get_fmax())
import matplotlib.pyplot as plt
from ase.io import iread
energies = []
distances = []
for atoms in iread('binding_curve.traj'):
energies.append(atoms.get_potential_energy())
distances.append(atoms.positions[1, 2] - atoms.positions[0, 2])
ax = plt.gca()
ax.plot(distances, energies)
ax.set_xlabel('Distance [Å]')
ax.set_ylabel('Total energy [eV]')
plt.show()
7.7056E+00 -3.4445E+00 2.0241E+00
2.4898E+00 -4.4453E+00 3.0289E+01
2.8009E+04 -4.5827E+04 3.0655E+05
""")
cells = []
cells.append(np.array([[23.01, -0.3943E-01, 0.4612E-01], [-0.9486E-01, 22.98, 0.4551], [0.6568, 0.7694, 19.21]]))
cells.append(np.array([[22.90, -0.3925E-01, 0.4591E-01], [-0.9443E-01, 22.88, 0.4531], [0.6538, 0.7660, 19.12]]))
cells.append(np.array([[22.73, -0.3896E-01, 0.4557E-01], [-0.9374E-01, 22.71, 0.4497], [0.6490, 0.7603, 18.98]]))
traj = aseIO.read(fd, format='dlp-history', index=slice(0,None))
assert len(traj) == 3
traj = aseIO.iread(fd, format='dlp-history', index=slice(0,None))
for i, frame in enumerate(traj):
assert len(frame) == 2
assert frame[0].symbol == 'O'
assert frame[1].symbol == 'Ni'
assert np.isclose(frame.get_cell(),cells[i]).all()
symbols = frame.get_chemical_symbols()
traj = iread_dlp_history(fd, symbols)
for i, frame in enumerate(traj):
assert len(frame) == 2
assert frame[0].symbol == 'O'
assert frame[1].symbol == 'Ni'
assert np.isclose(frame.get_cell(),cells[i]).all()
# Stripped (minimal) version of nomad entry with 3 images.
# The images are actually identical for some reason, but we want to be sure
# that they are extracted correctly.
nomad_data = """{"uri": "nmd://N9Jqc1y-Bzf7sI1R9qhyyyoIosJDs/C74RJltyQeM9_WFuJYO49AR4gKuJ2", "section_run": [{"name": "section_run", "uri": "nmd://N9Jqc1y-Bzf7sI1R9qhyyyoIosJDs/C74RJltyQeM9_WFuJYO49AR4gKuJ2/section_run/0c", "gIndex": 0, "section_system": [{"configuration_periodic_dimensions": [{"flatData": [true, true, true]}], "uri": "nmd://N9Jqc1y-Bzf7sI1R9qhyyyoIosJDs/C74RJltyQeM9_WFuJYO49AR4gKuJ2/section_run/0c/section_system/0c", "gIndex": 0, "lattice_vectors": {"flatData": [1.289082e-09, 0.0, 0.0, 0.0, 1.2238921e-09, 0.0, 0.0, 0.0, 1.103065e-09]}, "name": "section_system", "atom_positions": {"flatData": [1.8369957336276003e-10, 1.6999517355319901e-10, 0.0, 8.282405733627601e-10, 4.41950876446801e-10, 0.0, 4.6084142663724006e-10, 7.81941223553199e-10, 0.0, 1.1053824266372401e-09, 1.053896926446801e-09, 0.0, 6.44541e-10, 0.0, 1.2892214482885e-10, 0.0, 6.1194605e-10, 1.2892214482885e-10, 1.0605776359825801e-09, 1.98497527706708e-10, 2.5802171766295004e-10, 4.1603663598258003e-10, 4.13448522293292e-10, 2.5802171766295004e-10, 8.730453640174201e-10, 8.10443577706708e-10, 2.5802171766295004e-10, 2.2850436401742e-10, 1.025394572293292e-09, 2.5802171766295004e-10, 5.2517352791676e-10, 4.6602249641989005e-11, 5.515325e-10, 1.4685300778002002e-10, 1.66342369669381e-10, 5.515325e-10, 7.9139400778002e-10, 4.45603680330619e-10, 5.515325e-10, 1.1697145279167601e-09, 5.65343800358011e-10, 5.515325e-10, 1.1936747208324e-10, 6.58548299641989e-10, 5.515325e-10, 4.976879922199801e-10, 7.78288419669381e-10, 5.515325e-10, 1.14222899221998e-09, 1.057549730330619e-09, 5.515325e-10, 7.6390847208324e-10, 1.177289850358011e-09, 5.515325e-10, 1.0605776359825801e-09, 1.98497527706708e-10, 8.450432823370501e-10, 4.1603663598258003e-10, 4.13448522293292e-10, 8.450432823370501e-10, 8.730453640174201e-10, 8.10443577706708e-10, 8.450432823370501e-10, 2.2850436401742e-10, 1.025394572293292e-09, 8.450432823370501e-10, 6.44541e-10, 0.0, 9.7414285517115e-10, 0.0, 6.1194605e-10, 9.7414285517115e-10, 9.1056464993268e-10, 1.25465107864641e-10, 0.0, 5.4589940120358e-10, 2.7689366691594603e-10, 0.0, 1.19044040120358e-09, 3.35052383084054e-10, 0.0, 2.6602364993268e-10, 4.86480942135359e-10, 0.0, 1.0230583500673202e-09, 7.37411157864641e-10, 0.0, 9.864159879642e-11, 8.88839716915946e-10, 0.0, 7.431825987964201e-10, 9.469984330840542e-10, 0.0, 3.7851735006732003e-10, 1.098426992135359e-09, 0.0, 0.0, 0.0, 2.0246975378805e-10, 6.44541e-10, 6.1194605e-10, 2.0246975378805e-10, 3.4046283730566e-10, 1.17495575349518e-10, 3.040167815311e-10, 9.8500383730566e-10, 4.944504746504821e-10, 3.040167815311e-10, 3.0407816269434e-10, 7.29441625349518e-10, 3.040167815311e-10, 9.486191626943402e-10, 1.106396524650482e-09, 3.040167815311e-10, 7.3089033824148e-10, 2.12568186818173e-10, 3.5617527624000004e-10, 8.634933824148001e-11, 3.9937786318182705e-10, 3.5617527624000004e-10, 1.2027326617585201e-09, 8.24514236818173e-10, 3.5617527624000004e-10, 5.581916617585201e-10, 1.011323913181827e-09, 3.5617527624000004e-10, 1.11810587223036e-09, 1.51565512577295e-10, 5.515325e-10, 4.7356487223036e-10, 4.60380537422705e-10, 5.515325e-10, 8.155171277696401e-10, 7.63511562577295e-10, 5.515325e-10, 1.7097612776964e-10, 1.072326587422705e-09, 5.515325e-10, 7.3089033824148e-10, 2.12568186818173e-10, 7.468897237600001e-10, 8.634933824148001e-11, 3.9937786318182705e-10, 7.468897237600001e-10, 1.2027326617585201e-09, 8.24514236818173e-10, 7.468897237600001e-10, 5.581916617585201e-10, 1.011323913181827e-09, 7.468897237600001e-10, 3.4046283730566e-10, 1.17495575349518e-10, 7.990482184689e-10, 9.8500383730566e-10, 4.944504746504821e-10, 7.990482184689e-10, 3.0407816269434e-10, 7.29441625349518e-10, 7.990482184689e-10, 9.486191626943402e-10, 1.106396524650482e-09, 7.990482184689e-10, 0.0, 0.0, 9.005952462119501e-10, 6.44541e-10, 6.1194605e-10, 9.005952462119501e-10]}, "atom_species": [33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38]}, {"configuration_periodic_dimensions": [{"flatData": [true, true, true]}], "uri": "nmd://N9Jqc1y-Bzf7sI1R9qhyyyoIosJDs/C74RJltyQeM9_WFuJYO49AR4gKuJ2/section_run/0c/section_system/1c", "lattice_vectors": {"flatData": [1.289082e-09, 0.0, 0.0, 0.0, 1.2238921e-09, 0.0, 0.0, 0.0, 1.103065e-09]}, "gIndex": 1, "name": "section_system", "atom_positions": {"flatData": [1.8369957336276003e-10, 1.6999517355319901e-10, 0.0, 8.282405733627601e-10, 4.41950876446801e-10, 0.0, 4.6084142663724006e-10, 7.81941223553199e-10, 0.0, 1.1053824266372401e-09, 1.053896926446801e-09, 0.0, 6.44541e-10, 0.0, 1.2892214482885e-10, 0.0, 6.1194605e-10, 1.2892214482885e-10, 1.0605776359825801e-09, 1.98497527706708e-10, 2.5802171766295004e-10, 4.1603663598258003e-10, 4.13448522293292e-10, 2.5802171766295004e-10, 8.730453640174201e-10, 8.10443577706708e-10, 2.5802171766295004e-10, 2.2850436401742e-10, 1.025394572293292e-09, 2.5802171766295004e-10, 5.2517352791676e-10, 4.6602249641989005e-11, 5.515325e-10, 1.4685300778002002e-10, 1.66342369669381e-10, 5.515325e-10, 7.9139400778002e-10, 4.45603680330619e-10, 5.515325e-10, 1.1697145279167601e-09, 5.65343800358011e-10, 5.515325e-10, 1.1936747208324e-10, 6.58548299641989e-10, 5.515325e-10, 4.976879922199801e-10, 7.78288419669381e-10, 5.515325e-10, 1.14222899221998e-09, 1.057549730330619e-09, 5.515325e-10, 7.6390847208324e-10, 1.177289850358011e-09, 5.515325e-10, 1.0605776359825801e-09, 1.98497527706708e-10, 8.450432823370501e-10, 4.1603663598258003e-10, 4.13448522293292e-10, 8.450432823370501e-10, 8.730453640174201e-10, 8.10443577706708e-10, 8.450432823370501e-10, 2.2850436401742e-10, 1.025394572293292e-09, 8.450432823370501e-10, 6.44541e-10, 0.0, 9.7414285517115e-10, 0.0, 6.1194605e-10, 9.7414285517115e-10, 9.1056464993268e-10, 1.25465107864641e-10, 0.0, 5.4589940120358e-10, 2.7689366691594603e-10, 0.0, 1.19044040120358e-09, 3.35052383084054e-10, 0.0, 2.6602364993268e-10, 4.86480942135359e-10, 0.0, 1.0230583500673202e-09, 7.37411157864641e-10, 0.0, 9.864159879642e-11, 8.88839716915946e-10, 0.0, 7.431825987964201e-10, 9.469984330840542e-10, 0.0, 3.7851735006732003e-10, 1.098426992135359e-09, 0.0, 0.0, 0.0, 2.0246975378805e-10, 6.44541e-10, 6.1194605e-10, 2.0246975378805e-10, 3.4046283730566e-10, 1.17495575349518e-10, 3.040167815311e-10, 9.8500383730566e-10, 4.944504746504821e-10, 3.040167815311e-10, 3.0407816269434e-10, 7.29441625349518e-10, 3.040167815311e-10, 9.486191626943402e-10, 1.106396524650482e-09, 3.040167815311e-10, 7.3089033824148e-10, 2.12568186818173e-10, 3.5617527624000004e-10, 8.634933824148001e-11, 3.9937786318182705e-10, 3.5617527624000004e-10, 1.2027326617585201e-09, 8.24514236818173e-10, 3.5617527624000004e-10, 5.581916617585201e-10, 1.011323913181827e-09, 3.5617527624000004e-10, 1.11810587223036e-09, 1.51565512577295e-10, 5.515325e-10, 4.7356487223036e-10, 4.60380537422705e-10, 5.515325e-10, 8.155171277696401e-10, 7.63511562577295e-10, 5.515325e-10, 1.7097612776964e-10, 1.072326587422705e-09, 5.515325e-10, 7.3089033824148e-10, 2.12568186818173e-10, 7.468897237600001e-10, 8.634933824148001e-11, 3.9937786318182705e-10, 7.468897237600001e-10, 1.2027326617585201e-09, 8.24514236818173e-10, 7.468897237600001e-10, 5.581916617585201e-10, 1.011323913181827e-09, 7.468897237600001e-10, 3.4046283730566e-10, 1.17495575349518e-10, 7.990482184689e-10, 9.8500383730566e-10, 4.944504746504821e-10, 7.990482184689e-10, 3.0407816269434e-10, 7.29441625349518e-10, 7.990482184689e-10, 9.486191626943402e-10, 1.106396524650482e-09, 7.990482184689e-10, 0.0, 0.0, 9.005952462119501e-10, 6.44541e-10, 6.1194605e-10, 9.005952462119501e-10]}, "atom_species": [33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38]}, {"configuration_periodic_dimensions": [{"flatData": [true, true, true]}], "uri": "nmd://N9Jqc1y-Bzf7sI1R9qhyyyoIosJDs/C74RJltyQeM9_WFuJYO49AR4gKuJ2/section_run/0c/section_system/2c", "gIndex": 2, "lattice_vectors": {"flatData": [1.289082e-09, 0.0, 0.0, 0.0, 1.2238921e-09, 0.0, 0.0, 0.0, 1.103065e-09]}, "name": "section_system", "atom_positions": {"flatData": [1.8369957336276003e-10, 1.6999517355319901e-10, 0.0, 8.282405733627601e-10, 4.41950876446801e-10, 0.0, 4.6084142663724006e-10, 7.81941223553199e-10, 0.0, 1.1053824266372401e-09, 1.053896926446801e-09, 0.0, 6.44541e-10, 0.0, 1.2892214482885e-10, 0.0, 6.1194605e-10, 1.2892214482885e-10, 1.0605776359825801e-09, 1.98497527706708e-10, 2.5802171766295004e-10, 4.1603663598258003e-10, 4.13448522293292e-10, 2.5802171766295004e-10, 8.730453640174201e-10, 8.10443577706708e-10, 2.5802171766295004e-10, 2.2850436401742e-10, 1.025394572293292e-09, 2.5802171766295004e-10, 5.2517352791676e-10, 4.6602249641989005e-11, 5.515325e-10, 1.4685300778002002e-10, 1.66342369669381e-10, 5.515325e-10, 7.9139400778002e-10, 4.45603680330619e-10, 5.515325e-10, 1.1697145279167601e-09, 5.65343800358011e-10, 5.515325e-10, 1.1936747208324e-10, 6.58548299641989e-10, 5.515325e-10, 4.976879922199801e-10, 7.78288419669381e-10, 5.515325e-10, 1.14222899221998e-09, 1.057549730330619e-09, 5.515325e-10, 7.6390847208324e-10, 1.177289850358011e-09, 5.515325e-10, 1.0605776359825801e-09, 1.98497527706708e-10, 8.450432823370501e-10, 4.1603663598258003e-10, 4.13448522293292e-10, 8.450432823370501e-10, 8.730453640174201e-10, 8.10443577706708e-10, 8.450432823370501e-10, 2.2850436401742e-10, 1.025394572293292e-09, 8.450432823370501e-10, 6.44541e-10, 0.0, 9.7414285517115e-10, 0.0, 6.1194605e-10, 9.7414285517115e-10, 9.1056464993268e-10, 1.25465107864641e-10, 0.0, 5.4589940120358e-10, 2.7689366691594603e-10, 0.0, 1.19044040120358e-09, 3.35052383084054e-10, 0.0, 2.6602364993268e-10, 4.86480942135359e-10, 0.0, 1.0230583500673202e-09, 7.37411157864641e-10, 0.0, 9.864159879642e-11, 8.88839716915946e-10, 0.0, 7.431825987964201e-10, 9.469984330840542e-10, 0.0, 3.7851735006732003e-10, 1.098426992135359e-09, 0.0, 0.0, 0.0, 2.0246975378805e-10, 6.44541e-10, 6.1194605e-10, 2.0246975378805e-10, 3.4046283730566e-10, 1.17495575349518e-10, 3.040167815311e-10, 9.8500383730566e-10, 4.944504746504821e-10, 3.040167815311e-10, 3.0407816269434e-10, 7.29441625349518e-10, 3.040167815311e-10, 9.486191626943402e-10, 1.106396524650482e-09, 3.040167815311e-10, 7.3089033824148e-10, 2.12568186818173e-10, 3.5617527624000004e-10, 8.634933824148001e-11, 3.9937786318182705e-10, 3.5617527624000004e-10, 1.2027326617585201e-09, 8.24514236818173e-10, 3.5617527624000004e-10, 5.581916617585201e-10, 1.011323913181827e-09, 3.5617527624000004e-10, 1.11810587223036e-09, 1.51565512577295e-10, 5.515325e-10, 4.7356487223036e-10, 4.60380537422705e-10, 5.515325e-10, 8.155171277696401e-10, 7.63511562577295e-10, 5.515325e-10, 1.7097612776964e-10, 1.072326587422705e-09, 5.515325e-10, 7.3089033824148e-10, 2.12568186818173e-10, 7.468897237600001e-10, 8.634933824148001e-11, 3.9937786318182705e-10, 7.468897237600001e-10, 1.2027326617585201e-09, 8.24514236818173e-10, 7.468897237600001e-10, 5.581916617585201e-10, 1.011323913181827e-09, 7.468897237600001e-10, 3.4046283730566e-10, 1.17495575349518e-10, 7.990482184689e-10, 9.8500383730566e-10, 4.944504746504821e-10, 7.990482184689e-10, 3.0407816269434e-10, 7.29441625349518e-10, 7.990482184689e-10, 9.486191626943402e-10, 1.106396524650482e-09, 7.990482184689e-10, 0.0, 0.0, 9.005952462119501e-10, 6.44541e-10, 6.1194605e-10, 9.005952462119501e-10]}, "atom_species": [33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38]}]}], "name": "calculation_context"}"""
fname = 'nmd.test.nomad-json'
with open(fname, 'w') as fd:
fd.write(nomad_data)
from ase.io import iread
images = list(iread(fname))
assert len(images) == 3
for atoms in images:
assert all(atoms.pbc)
assert (atoms.cell > 0).sum() == 3
assert atoms.get_chemical_formula() == 'As24Sr32'
# Code for cleaning up nomad files so their size is reasonable for inclusion
# in test suite:
"""
ourkeys = {'section_run', 'section_system', 'name', 'atom_species',
'atom_positions', 'flatData', 'uri', 'gIndex',
'configuration_periodic_dimensions', 'lattice_vectors'}
includekeys = lambda k: k in ourkeys
fname = ...
#!/usr/bin/env python3
from ase.io import iread,write
import sys
if len(sys.argv) > 1:
intv = int(sys.argv[1]) # output every intv steps
fname = sys.argv[2]
else:
intv = 1
fname = 'vasprun1.xml'
a = iread(fname)
for i,j in enumerate(a):
if i % intv == 0:
write('XDATCAR.xyz',j,append=True)
#!/usr/bin/env python3
'''
This is a script to plot temperature, energies, pressure, lattice constants
as a function of vasp md steps. This requires XDATCAR, vasprun.xml, OSZICAR
files. This is for single md run, use vasp_md_total.py for multiple runs.
'''
import numpy as np
import matplotlib.pyplot as plt
import os.path
from ase.io import iread
# read vasprun.xml with ase.io.iread
a = iread('vasprun.xml')
latc = []
vol = []
pres = [] # this pressure is not correct for md
for i in a:
latc.append(i.get_cell_lengths_and_angles())
vol.append(i.get_volume())
pres.append(i.get_stress())
# pressure includes kinetic contribution, obatained by following bash command
# LC_ALL=C fgrep "Total+kin" OUTCAR > total+kin.dat
if os.path.exists('total+kin.dat'):
pres = []
with open('total+kin.dat', 'r') as f:
lines = f.readlines()
for i, j in enumerate(lines):
x = [float(y) for y in j.split()[1:]]
x.insert(0, i)
