def test_wrap_oplist(self, setup):
R, param = [0.1, 1.5], [1, 2.1]
H = Hamiltonian(setup.g.copy())
H.construct([R, param])
bz = MonkhorstPack(H, [10, 10, 1])
E = np.linspace(-4, 4, 1000)
dist = get_distribution('gaussian', smearing=0.05)
def wrap(es, parent, k, weight):
DOS = es.DOS(E, distribution=dist)
PDOS = es.PDOS(E, distribution=dist)
vel = es.velocity() * es.occupation().reshape(-1, 1)
return oplist([DOS, PDOS, vel])
bz.asaverage()
results = bz.eigenstate(wrap=wrap)
assert np.allclose(bz.DOS(E, distribution=dist), results[0])
assert np.allclose(bz.PDOS(E, distribution=dist), results[1])
def test_as_wrap_default_oplist(self):
from sisl import geom, Hamiltonian
g = geom.graphene()
H = Hamiltonian(g)
H.construct([[0.1, 1.44], [0, -2.7]])
bz = MonkhorstPack(H, [2, 2, 2], trs=False).asaverage()
assert len(bz) == 2 ** 3
# Check with a wrap function
E = np.linspace(-2, 2, 100)
def wrap_sum(es, weight):
PDOS = es.PDOS(E) * weight
return PDOS.sum(0), PDOS
DOS, PDOS = bz.assum().eigenstate(wrap=wrap_sum)
assert np.allclose(bz.DOS(E), DOS)
assert np.allclose(bz.PDOS(E), PDOS)
