def calculate_chirality(tag, center, radius, overwrite=False, **kwargs):
# converting the Model to the pickle format (which is quicker to load)
# Note that keeping only the pickle format is dangerous, because it
# may become unreadable -- use the JSON format for long-term saving.
try:
with open(MODEL_PATH, 'rb') as f:
model = pickle.load(f)
except IOError:
# The .xz compression is used to avoid the GitHub file size limit
with lzma.open(MODEL_SOURCE + '.xz') as fin, open(MODEL_SOURCE,
'wb') as fout:
fout.write(fin.read())
model = Model.from_json_file(MODEL_SOURCE)
with open(MODEL_PATH, 'wb') as f:
pickle.dump(model, f, protocol=pickle.HIGHEST_PROTOCOL)
system = z2pack.tb.System(model)
full_name = MODEL_NAME + '_' + tag
res = z2pack.surface.run(system=system,
surface=z2pack.shape.Sphere(center, radius),
save_file=os.path.join('results',
full_name + '.p'),
load=not overwrite,
**kwargs)
# Again, the pickle format is used because it is faster than JSON
# or msgpack. If you wish to permanently store the result, use
# z2pack.io.save(res, os.path.join('results', full_name + '.json'))
print('Chern number:', z2pack.invariant.chern(res))
def __init__(self, folder, prefix):
"""
folder # The folder containing the Wannier function files
prefix, # The prefix of Wannier90 outputs. e.g. wannier90_up
"""
m = Model.from_wannier_folder(folder=folder, prefix=prefix)
self.model = ijR.from_tbmodel(m)
def from_tbmodel_hdf5(fname):
from tbmodels import Model
m = Model.from_hdf5_file(fname)
ret = ijR(nbasis=m.size)
for R, v in m.hop.items():
ret.data[R] = v
ret.positions = np.reshape(m.pos, (m.size, 3))
return ret
def from_tbmodel_hdf5(fname):
"""
load model from a hdf5 file. It uses the tbmodel parser.
"""
from tbmodels import Model
m = Model.from_hdf5_file(fname)
ret = MyTB(nbasis=m.size)
for R, v in m.hop.items():
ret.data[R] = v
ret.positions = np.reshape(m.pos, (m.size, m.ndim))
return ret
def save_Wannier_model():
m = Model.from_wannier_folder(folder='/home/hexu/projects/SMO_Wannier/',
prefix='abinito_w90_down')
mijR = ijR.from_tbmodel(m)
mijR.save(fname='SMO_tb.nc')