def test_init(self):
filepath = os.path.join(test_dir, 'CHGCAR.nospin')
chg = Chgcar.from_file(filepath)
self.assertAlmostEqual(chg.get_integrated_diff(0, 2)[0, 1], 0)
filepath = os.path.join(test_dir, 'CHGCAR.spin')
chg = Chgcar.from_file(filepath)
self.assertAlmostEqual(chg.get_integrated_diff(0, 1)[0, 1],
-0.0043896932237534022)
#test sum
chg += chg
self.assertAlmostEqual(chg.get_integrated_diff(0, 1)[0, 1],
-0.0043896932237534022 * 2)
filepath = os.path.join(test_dir, 'CHGCAR.noncubic')
chg = Chgcar.from_file(filepath)
ans = [0.221423, 0.462059, 0.470549, 0.434775, 0.860738, 2.1717482]
myans = chg.get_integrated_diff(0, 3, 6)
self.assertTrue(np.allclose(myans[:, 1], ans))
def test_init(self):
filepath = os.path.join(test_dir, 'CHGCAR.nospin')
chg = Chgcar.from_file(filepath)
self.assertAlmostEqual(chg.get_integrated_diff(0, 2)[0, 1], 0)
filepath = os.path.join(test_dir, 'CHGCAR.spin')
chg = Chgcar.from_file(filepath)
self.assertAlmostEqual(chg.get_integrated_diff(0, 1)[0, 1],
-0.0043896932237534022)
#test sum
chg += chg
self.assertAlmostEqual(chg.get_integrated_diff(0, 1)[0, 1],
-0.0043896932237534022 * 2)
filepath = os.path.join(test_dir, 'CHGCAR.Fe3O4')
chg = Chgcar.from_file(filepath)
ans = [1.93313368, 3.91201473, 4.11858277, 4.1240093, 4.10634989,
3.38864822]
myans = chg.get_integrated_diff(0, 3, 6)
self.assertTrue(np.allclose(myans[:, 1], ans))
def test_init(self):
filepath = os.path.join(test_dir, 'CHGCAR.nospin')
chg = Chgcar.from_file(filepath)
self.assertAlmostEqual(chg.get_integrated_diff(0, 2)[0, 1], 0)
filepath = os.path.join(test_dir, 'CHGCAR.spin')
chg = Chgcar.from_file(filepath)
self.assertAlmostEqual(chg.get_integrated_diff(0, 1)[0, 1],
-0.0043896932237534022)
#test sum
chg += chg
self.assertAlmostEqual(chg.get_integrated_diff(0, 1)[0, 1],
-0.0043896932237534022 * 2)
filepath = os.path.join(test_dir, 'CHGCAR.Fe3O4')
chg = Chgcar.from_file(filepath)
ans = [1.93313368, 3.91201473, 4.11858277, 4.1240093, 4.10634989,
3.38864822]
myans = chg.get_integrated_diff(0, 3, 6)
self.assertTrue(np.allclose(myans[:, 1], ans))
def __init__(self, chgcar_filename, potcar_filename=None):
"""
Args:
chgcar_filename:
The filename of the CHGCAR.
potcar_filename:
Optional: the filename of the corresponding POTCAR file. Used
for calculating the charge transfer. If None, the
get_charge_transfer method will raise a ValueError.
"""
temp_dir = tempfile.mkdtemp()
self.chgcar = Chgcar.from_file(chgcar_filename)
self.potcar = Potcar.from_file(potcar_filename) \
if potcar_filename is not None else None
self.natoms = self.chgcar.poscar.natoms
try:
shutil.copy(chgcar_filename, os.path.join(temp_dir, "CHGCAR"))
current_dir = os.getcwd()
os.chdir(temp_dir)
rs = subprocess.Popen(["bader", "CHGCAR"],
stdout=subprocess.PIPE,
stdin=subprocess.PIPE,
close_fds=True)
rs.communicate()
data = []
with open("ACF.dat") as f:
raw = f.readlines()
headers = [s.lower() for s in raw.pop(0).split()]
raw.pop(0)
while True:
l = raw.pop(0).strip()
if l.startswith("-"):
break
vals = map(float, l.split()[1:])
data.append(dict(zip(headers[1:], vals)))
for l in raw:
toks = l.strip().split(":")
if toks[0] == "VACUUM CHARGE":
self.vacuum_charge = float(toks[1])
elif toks[0] == "VACUUM VOLUME":
self.vacuum_volume = float(toks[1])
elif toks[0] == "NUMBER OF ELECTRONS":
self.nelectrons = float(toks[1])
self.data = data
os.chdir(current_dir)
except Exception as ex:
print str(ex)
finally:
shutil.rmtree(temp_dir)
def __init__(self, chgcar_filename, potcar_filename=None):
"""
Args:
chgcar_filename:
The filename of the CHGCAR.
potcar_filename:
Optional: the filename of the corresponding POTCAR file. Used
for calculating the charge transfer. If None, the
get_charge_transfer method will raise a ValueError.
"""
temp_dir = tempfile.mkdtemp()
self.chgcar = Chgcar.from_file(chgcar_filename)
self.potcar = Potcar.from_file(potcar_filename) \
if potcar_filename is not None else None
self.natoms = self.chgcar.poscar.natoms
try:
shutil.copy(chgcar_filename, os.path.join(temp_dir, "CHGCAR"))
current_dir = os.getcwd()
os.chdir(temp_dir)
rs = subprocess.Popen(["bader", "CHGCAR"],
stdout=subprocess.PIPE,
stdin=subprocess.PIPE, close_fds=True)
rs.communicate()
data = []
with open("ACF.dat") as f:
raw = f.readlines()
headers = [s.lower() for s in raw.pop(0).split()]
raw.pop(0)
while True:
l = raw.pop(0).strip()
if l.startswith("-"):
break
vals = map(float, l.split()[1:])
data.append(dict(zip(headers[1:], vals)))
for l in raw:
toks = l.strip().split(":")
if toks[0] == "VACUUM CHARGE":
self.vacuum_charge = float(toks[1])
elif toks[0] == "VACUUM VOLUME":
self.vacuum_volume = float(toks[1])
elif toks[0] == "NUMBER OF ELECTRONS":
self.nelectrons = float(toks[1])
self.data = data
os.chdir(current_dir)
except Exception as ex:
print str(ex)
finally:
shutil.rmtree(temp_dir)
parser = argparse.ArgumentParser()
parser.add_argument("-s1", type=str, default="", help="POSCAR for endpoint 1")
parser.add_argument("-s2", type=str, default="", help="POSCAR for endpoint 2")
parser.add_argument("-chg", type=str, default="", help="CHGCAR for interpolation (if not specified, use free volume metric)")
parser.add_argument("-o", type=str, default="", help="Output POSCAR with images superimposed")
args = parser.parse_args()
s1 = Poscar.from_file(args.s1).structure
s2 = Poscar.from_file(args.s2).structure
# Find diffusing species site indices
mg_sites = []
for site_i, site in enumerate(s1.sites):
if site.specie == Element("Mg"):
mg_sites.append(site_i)
# Interpolate
if args.chg != "":
print("Using CHGCAR potential mode.")
chg = Chgcar.from_file(args.chg)
pf = NEBPathfinder(s1, s2, relax_sites=mg_sites, v=ChgcarPotential(chg).get_v(), n_images=10)
else:
print("Using free volume mode")
print("WARNING: This mode is not optimized and a lot of numerical parameters are not good, so the output might be bad!")
pf = NEBPathfinder(s1, s2, relax_sites=mg_sites, v=FreeVolumePotential(s1, (40,40,40)).get_v(), n_images=10, h=0.001)
# Get images
images = pf.images
# Plot path
pf.plot_images(args.o)
def main():
# Set default variables
options = dict(
spin_channel='total',
center_cartesian=[0., 0., 0.],
x_axis=[1., 0., 0.],
y_axis=[0., 0., 1.],
x_repeat=1, # approximate number of unit cells to interpolate
y_repeat=1, # approximate number of unit cells to interpolate
x_res=500, # points to interpolate
y_res=500, # points to interpolate
format='xyz',
)
options.update(**vars(_build_parser().parse_args()))
chg = Chgcar.from_file(options['filename'])
structure = chg.structure
rprim = chg.structure.lattice.matrix
# Decide where to obtain center from
# Cartesian option set
center = options['center_direct']
if options['center_cartesian'] is not None:
# Convert from cartesian to reduced coordinates
center = numpy.linalg.solve(rprim, options['center_cartesian']).tolist()
# Atomic option is set
if options['center_atom'] is not None:
# Convert from atom number to reduced coordinates
center = structure[options['center_atom']].frac_coords.tolist()
plane = numpy.array([options['x_axis'], options['y_axis']], dtype=float)
repeat = [options['x_repeat'], options['y_repeat']]
res = [options['x_res'], options['y_res']]
data = 0
if options['spin_channel'] == 'total':
data = chg.data['total']
if options['spin_channel'] == 'up':
data = chg.spin_data[Spin.up]
if options['spin_channel'] == 'down':
data = chg.spin_data[Spin.down]
if options['spin_channel'] == 'polarization':
data = chg.spin_data[Spin.up] - chg.spin_data[Spin.down]
data /= structure.volume
cs = plane_to_cs(plane)
posinplane = numpy.dot(cs, numpy.dot(structure.frac_coords - center, rprim).T).T
print('Atom positions in plane:')
for i, pos in enumerate(posinplane):
print(' %s: %f %f (z = %f)' % (structure[i].specie, pos[0], pos[1], pos[2]))
# Interpolate
x, y, z = interpolate_plane(data, rprim, plane=plane, center=center, dim=repeat, res=res)
print('Cell density:\n minimum: %f\n maximum: %f' % (data.min(), data.max()))
print('In-plane density:\n minimum: %f\n maximum: %f' % (z.min(), z.max()))
print('Note: PAW calculations may contain negative values for the pseudo-density in the core regions.')
suffix = '.gz' if options['gzip'] else ''
if options['format'] == 'xyz':
out = numpy.array([x.flatten(), y.flatten(), z.flatten()]).T
with open('chg_%s_in_plane.csv%s' % (options['spin_channel'], suffix), 'w+') as f:
numpy.savetxt(f, out, delimiter=',', header=' X, Y, Z')
if options['format'] == 'matrix':
with open('chg_%s_in_plane.mat%s' % (options['spin_channel'], suffix), 'w+') as f:
numpy.savetxt(f, z, delimiter=' ', header=' Z matrix')
if options['format'] == 'image':
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
fig = plt.figure()
plt.contour(x, y, z, options['contour'], colors='k')
fig.savefig(options['out'])
default="",
help="Output POSCAR with images superimposed")
args = parser.parse_args()
s1 = Poscar.from_file(args.s1).structure
s2 = Poscar.from_file(args.s2).structure
# Find diffusing species site indices
mg_sites = []
for site_i, site in enumerate(s1.sites):
if site.specie == Element("Mg"):
mg_sites.append(site_i)
# Interpolate
if args.chg != "":
print("Using CHGCAR potential mode.")
chg = Chgcar.from_file(args.chg)
pf = NEBPathfinder(s1,
s2,
relax_sites=mg_sites,
v=ChgcarPotential(chg).get_v(),
n_images=10)
else:
print("Using free volume mode")
print(
"WARNING: This mode is not optimized and a lot of numerical parameters are not good, so the output might be bad!"
)
pf = NEBPathfinder(s1,
s2,
relax_sites=mg_sites,
v=FreeVolumePotential(s1, (40, 40, 40)).get_v(),
n_images=10,