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(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)
assert len(bz) == 2 ** 3
# Check with a wrap function
def wrap_reverse(arg):
return arg[::-1]
asarray = bz.asarray().eigh(wrap=wrap_reverse)
aslist = np.array(bz.aslist().eigh(wrap=wrap_reverse))
asyield = np.array([a for a in bz.asyield().eigh(wrap=wrap_reverse)])
asaverage = bz.asaverage().eigh(wrap=wrap_reverse)
assert np.allclose(asarray, aslist)
assert np.allclose(asarray, asyield)
# Average needs to be performed
assert np.allclose((asarray / len(bz)).sum(0), asaverage)
# Now we should check whether the reverse is doing its magic!
mylist = [wrap_reverse(H.eigh(k=k)) for k in bz]
assert np.allclose(aslist, mylist)
def test_wrap_kwargs(arg):
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)
assert len(bz) == 2 ** 3
def wrap_none(arg):
return arg
def wrap_kwargs(arg, parent, k, weight):
return arg * weight
E = np.linspace(-2, 2, 100)
asarray1 = (bz.asarray().DOS(E, wrap=wrap_none) * bz.weight.reshape(-1, 1)).sum(0)
asarray2 = bz.asarray().DOS(E, wrap=wrap_kwargs).sum(0)
aslist1 = (np.array(bz.aslist().DOS(E, wrap=wrap_none)) * bz.weight.reshape(-1, 1)).sum(0)
aslist2 = np.array(bz.aslist().DOS(E, wrap=wrap_kwargs)).sum(0)
asyield1 = (np.array([a for a in bz.asyield().DOS(E, wrap=wrap_none)]) * bz.weight.reshape(-1, 1)).sum(0)
asyield2 = np.array([a for a in bz.asyield().DOS(E, wrap=wrap_kwargs)]).sum(0)
asaverage = bz.asaverage().DOS(E, wrap=wrap_none)
assum = bz.assum().DOS(E, wrap=wrap_kwargs)
assert np.allclose(asarray1, asaverage)
assert np.allclose(asarray2, asaverage)
assert np.allclose(aslist1, asaverage)
assert np.allclose(aslist2, asaverage)
assert np.allclose(asyield1, asaverage)
assert np.allclose(asyield2, asaverage)
assert np.allclose(assum, asaverage)
def test_class3(self, setup):
class Test(SuperCellChild):
def __init__(self, sc):
self.set_supercell(sc)
def eigh(self, k, *args, **kwargs):
return np.arange(3)
def eig(self, k, *args, **kwargs):
return np.arange(3) - 1
bz = MonkhorstPack(Test(setup.s1), [2] * 3)
# Try the yield method
bz.asyield()
for val in bz.eigh():
assert np.allclose(val, np.arange(3))
for val in bz.eig():
assert np.allclose(val, np.arange(3) - 1)
# Average
assert np.allclose(bz.asaverage().eigh(), np.arange(3))
def test_as_simple(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)
assert len(bz) == 2 ** 3
# Assert that as* all does the same
asarray = bz.asarray().eigh()
aslist = np.array(bz.aslist().eigh())
asyield = np.array([a for a in bz.asyield().eigh()])
asaverage = bz.asaverage().eigh()
assert np.allclose(asarray, aslist)
assert np.allclose(asarray, asyield)
# Average needs to be performed
assert np.allclose((asarray / len(bz)).sum(0), asaverage)
