def phonopy_dos(filename, poscar='POSCAR', atoms=None):
"""Loads phonopy DoS and collates data per atom and as a total.
By default reads atom names from a POSCAR, but can be overridden to
allow for separation of environments.
Arguments
---------
filename : str
path to phonopy projected_dos.dat or similar.
poscar : str, optional
path to POSCAR. Ignored if atoms specified. Default: POSCAR.
atoms : str or array-like, optional
atoms in POSCAR order. Atom names can be repeated, in which
case their contributions are summed. Numbers can indicate
repetition in the manner of a chemical formula, so the
following are all acceptable and equivalent: "Ba 1 Sn 2 O 3",
"Ba Sn Sn O O O", "Ba Sn O 3". Different environments can be
distinguised with different atom names.
Default: read from POSCAR.
Returns
-------
dict
frequency, DoS per atom and total.
"""
# load data
data = np.transpose(np.loadtxt(filename))
units = tp.settings.units()
data2 = {
'frequency': data[0],
'meta': {
'phonon_dos_source': 'phonopy',
'units': {
'frequency': units['frequency']
}
}
}
conversions = settings.phonopy_conversions()
if 'frequency' in conversions:
data2['frequency'] *= conversions['frequency']
if atoms is None:
from pymatgen.io.vasp.inputs import Poscar
poscar = Poscar.from_file(poscar,
check_for_POTCAR=False,
read_velocities=False).as_dict()
atoms = [p['label'] for p in poscar['structure']['sites']]
elif isinstance(atoms, str):
atoms = atoms.split()
# combine atoms contributions
i = 0
n = 1
while i < len(atoms):
try:
atoms[i + 1] = int(atoms[i + 1])
if atoms[i] in data2:
data2[atoms[i]] += np.sum(data[n:n + atoms[i + 1]], axis=0)
else:
data2[atoms[i]] = np.sum(data[n:n + atoms[i + 1]], axis=0)
n += atoms[i + 1]
i += 2
except Exception:
if atoms[i] in data2:
data2[atoms[i]] += data[n]
else:
data2[atoms[i]] = data[n]
n += 1
i += 1
data2['total'] = np.sum(data[1:], axis=0)
return data2
def phonopy_dispersion(filename, xdata=None):
"""Loads phonopy dispersion, and can scale the x values.
Scaling the x values is necessary to plot multiple dispersions on
the same axes.
Arguments
---------
filename : str
filepath.
xdata : dict, optional
data for the dispersion to scale this to. Should have the
same path, must have the same number of labels. Not
necessary if using tp.plot.phonons.add_multi. Default: None.
Returns
-------
dict
dispersion data.
"""
import yaml
# load data
with open(filename, 'r') as f:
data = yaml.safe_load(f)
x = [d['distance'] for d in data['phonon']]
qp = [q['q-position'] for q in data['phonon']]
d2, ticks = get_path(data)
eigs = [[b['frequency'] for b in p['band']] for p in data['phonon']]
# scale data to other path
if xdata is not None:
d1 = xdata['tick_position']
n = 0
for i, d0 in enumerate(x):
while n <= len(d2) and not (d0 >= d2[n] and d0 <= d2[n + 1]):
n += 1
x[i] = d1[n] + ((d0 - d2[n]) * (d1[n+1] - d1[n]) / \
(d2[n+1] - d2[n]))
else:
d1 = d2
conversions = settings.phonopy_conversions()
if 'frequency' in conversions:
eigs *= conversions['frequency']
units = tp.settings.units()
data2 = {
'x': x,
'qpoint': qp,
'frequency': eigs,
'tick_position': d1,
'tick_label': ticks,
'meta': {
'phonon_dispersion_source': 'phonopy',
'units': {
'frequency': units['frequency']
}
}
}
return data2