def __init__(self):
super(AddDrop, self).__init__()
self.term_in = pt.Source()
self.term_pass = pt.Detector()
self.term_add = pt.Detector()
self.term_drop = pt.Detector()
self.dc1 = pt.DirectionalCoupler()
self.dc2 = pt.DirectionalCoupler()
self.wg1 = pt.Waveguide()
self.wg2 = pt.Waveguide()
self.link("term_in:0", "0:dc1:2", "0:wg1:1", "1:dc2:3", "0:term_drop")
self.link("term_add:0", "2:dc2:0", "0:wg2:1", "3:dc1:1", "0:term_pass")
def conn():
""" default connector """
wg = pt.Waveguide()
s = pt.Source()
d = pt.Detector()
conn = wg["ab"] * s["a"] * d["b"]
return conn
def nw():
""" default network (source-waveguide-detector) """
with pt.Network() as nw:
nw.wg = pt.Waveguide(length=1e-5)
nw.s = pt.Source()
nw.d = pt.Detector()
nw.link("s:0", "0:wg:1", "0:d")
return nw
def test_agrawal_soa():
# fmt: off
# single delay too many in buffermask:
# target = np.array([0.00000, 0.00814, 0.10276, 0.04285, 0.02142, 0.12841, 0.03331, 0.04192, 0.14501, 0.02096])
# delay compensated in buffermask:
target = np.array([
0.00000, 0.01252, 0.10493, 0.03503, 0.02863, 0.12833, 0.02530, 0.05175,
0.14189, 0.01401
])
# fmt: on
with pt.Network() as nw:
nw.src = pt.Source()
nw.soa = pt.AgrawalSoa()
nw.det = pt.Detector()
nw.link("src:0", "0:soa:1", "0:det")
env = pt.Environment(dt=6e-13, num_t=150)
src = torch.tensor(
0.3 * np.sin(0.0001 * 2 * np.pi * env.t * env.c / env.wl),
dtype=torch.get_default_dtype(),
)[:, None, None, None]
with env:
det = nw(src)[:, 0, 0, 0].detach().cpu().numpy()
np.testing.assert_almost_equal(target, det[::15], decimal=5)
def test_termination(unw):
unw.terminate()
unw.terminate([pt.Source("src"), pt.Detector("det")])
if torch.cuda.is_available(): # pragma: no cover
unw.cuda()
unw.terminate()
def d():
""" default detector """
return pt.Detector()