def test_AxonMapSpatial(engine):
# AxonMapSpatial automatically sets `rho`, `axlambda`:
model = AxonMapSpatial(engine=engine, xystep=5)
# User can set `rho`:
model.rho = 123
npt.assert_equal(model.rho, 123)
model.build(rho=987)
npt.assert_equal(model.rho, 987)
# Converting ret <=> dva
npt.assert_almost_equal(model.ret2dva(0), 0)
npt.assert_almost_equal(model.dva2ret(0), 0)
# Nothing in, None out:
npt.assert_equal(model.predict_percept(ArgusI()), None)
# Zero in = zero out:
implant = ArgusI(stim=np.zeros(16))
percept = model.predict_percept(implant)
npt.assert_equal(isinstance(percept, Percept), True)
npt.assert_equal(percept.shape, list(model.grid.x.shape) + [1])
npt.assert_almost_equal(percept.data, 0)
# Multiple frames are processed independently:
model = AxonMapSpatial(engine=engine, rho=200, axlambda=100, xystep=5)
model.build()
percept = model.predict_percept(ArgusI(stim={'A1': [1, 0], 'B3': [0, 2]}))
npt.assert_equal(percept.shape, list(model.grid.x.shape) + [2])
pmax = percept.data.max(axis=(0, 1))
npt.assert_almost_equal(percept.data[2, 3, 0], pmax[0])
npt.assert_almost_equal(percept.data[2, 3, 1], 0)
npt.assert_almost_equal(percept.data[3, 4, 0], 0)
npt.assert_almost_equal(percept.data[3, 4, 1], pmax[1])
def test_AxonMapSpatial(engine):
# AxonMapSpatial automatically sets `rho`, `axlambda`:
model = AxonMapSpatial(engine=engine, xystep=5)
# User can set `rho`:
model.rho = 123
npt.assert_equal(model.rho, 123)
model.build(rho=987)
npt.assert_equal(model.rho, 987)
# Converting ret <=> dva
npt.assert_equal(isinstance(model.retinotopy, Watson2014Map), True)
npt.assert_almost_equal(model.retinotopy.ret2dva(0, 0), (0, 0))
npt.assert_almost_equal(model.retinotopy.dva2ret(0, 0), (0, 0))
model2 = AxonMapSpatial(retinotopy=Watson2014DisplaceMap())
npt.assert_equal(isinstance(model2.retinotopy, Watson2014DisplaceMap),
True)
# Nothing in, None out:
npt.assert_equal(model.predict_percept(ArgusI()), None)
# Zero in = zero out:
implant = ArgusI(stim=np.zeros(16))
percept = model.predict_percept(implant)
npt.assert_equal(isinstance(percept, Percept), True)
npt.assert_equal(percept.shape, list(model.grid.x.shape) + [1])
npt.assert_almost_equal(percept.data, 0)
npt.assert_equal(percept.time, None)
# Lambda cannot be too small:
with pytest.raises(ValueError):
AxonMapSpatial(axlambda=9).build()
# Multiple frames are processed independently:
model = AxonMapSpatial(engine=engine,
rho=200,
axlambda=100,
xystep=5,
xrange=(-20, 20),
yrange=(-15, 15))
model.build()
# Axon map jax predict_percept not implemented yet
if engine == 'jax':
with pytest.raises(NotImplementedError):
percept = model.predict_percept(
ArgusII(stim={
'A1': [1, 0],
'B3': [0, 2]
}))
return
percept = model.predict_percept(ArgusI(stim={'A1': [1, 0], 'B3': [0, 2]}))
npt.assert_equal(percept.shape, list(model.grid.x.shape) + [2])
pmax = percept.data.max(axis=(0, 1))
npt.assert_almost_equal(percept.data[2, 3, 0], pmax[0])
npt.assert_almost_equal(percept.data[2, 3, 1], 0)
npt.assert_almost_equal(percept.data[3, 4, 0], 0)
npt.assert_almost_equal(percept.data[3, 4, 1], pmax[1])
npt.assert_almost_equal(percept.time, [0, 1])
def test_AxonMapModel_predict_percept(engine):
model = AxonMapModel(xystep=0.55,
axlambda=100,
rho=100,
thresh_percept=0,
engine=engine,
xrange=(-20, 20),
yrange=(-15, 15),
n_axons=500)
model.build()
# Single-electrode stim:
img_stim = np.zeros(60)
img_stim[47] = 1
percept = model.predict_percept(ArgusII(stim=img_stim))
# Single bright pixel, rest of arc is less bright:
npt.assert_equal(np.sum(percept.data > 0.8), 1)
npt.assert_equal(np.sum(percept.data > 0.6), 2)
npt.assert_equal(np.sum(percept.data > 0.1), 7)
npt.assert_equal(np.sum(percept.data > 0.0001), 32)
# Overall only a few bright pixels:
npt.assert_almost_equal(np.sum(percept.data), 3.31321, decimal=3)
# Brightest pixel is in lower right:
npt.assert_almost_equal(percept.data[33, 46, 0], np.max(percept.data))
# Top half is empty:
npt.assert_almost_equal(np.sum(percept.data[:27, :, 0]), 0)
# Same for lower band:
npt.assert_almost_equal(np.sum(percept.data[39:, :, 0]), 0)
# Full Argus II with small lambda: 60 bright spots
model = AxonMapModel(engine='serial',
xystep=1,
rho=100,
axlambda=40,
xrange=(-20, 20),
yrange=(-15, 15),
n_axons=500)
model.build()
percept = model.predict_percept(ArgusII(stim=np.ones(60)))
# Most spots are pretty bright, but there are 2 dimmer ones (due to their
# location on the retina):
npt.assert_equal(np.sum(percept.data > 0.5), 28)
npt.assert_equal(np.sum(percept.data > 0.275), 56)
# Model gives same outcome as Spatial:
spatial = AxonMapSpatial(engine='serial', xystep=1, rho=100, axlambda=40)
spatial.build()
spatial_percept = model.predict_percept(ArgusII(stim=np.ones(60)))
npt.assert_almost_equal(percept.data, spatial_percept.data)
npt.assert_equal(percept.time, None)
def test_deepcopy_AxonMapSpatial():
original = AxonMapSpatial()
copied = copy.deepcopy(original)
# Assert these are two different objects
npt.assert_equal(id(original) != id(copied), True)
# Assert these objects are equivalent
npt.assert_equal(original.__dict__ == copied.__dict__, True)
npt.assert_equal(original == copied, True)
# Assert building one object does not affect the copied
original.build()
npt.assert_equal(copied.is_built, False)
npt.assert_equal(original.__dict__ != copied.__dict__, True)
# Assert destroying the original doesn't affect the copied
original = None
npt.assert_equal(copied is not None, True)
def test_AxonMapModel_predict_percept(engine):
model = AxonMapModel(xystep=0.55,
axlambda=100,
rho=100,
thresh_percept=0,
engine=engine,
xrange=(-20, 20),
yrange=(-15, 15),
n_axons=500)
model.build()
# Single-electrode stim:
img_stim = np.zeros(60)
img_stim[47] = 1
# Axon map jax predict_percept not implemented yet
if engine == 'jax':
with pytest.raises(NotImplementedError):
percept = model.predict_percept(ArgusII(stim=img_stim))
return
percept = model.predict_percept(ArgusII(stim=img_stim))
# Single bright pixel, rest of arc is less bright:
npt.assert_equal(np.sum(percept.data > 0.8), 1)
npt.assert_equal(np.sum(percept.data > 0.6), 2)
npt.assert_equal(np.sum(percept.data > 0.1), 7)
npt.assert_equal(np.sum(percept.data > 0.0001), 31)
# Overall only a few bright pixels:
npt.assert_almost_equal(np.sum(percept.data), 3.3087, decimal=3)
# Brightest pixel is in lower right:
npt.assert_almost_equal(percept.data[33, 46, 0], np.max(percept.data))
# Top half is empty:
npt.assert_almost_equal(np.sum(percept.data[:27, :, 0]), 0)
# Same for lower band:
npt.assert_almost_equal(np.sum(percept.data[39:, :, 0]), 0)
# Full Argus II with small lambda: 60 bright spots
model = AxonMapModel(engine='serial',
xystep=1,
rho=100,
axlambda=40,
xrange=(-20, 20),
yrange=(-15, 15),
n_axons=500)
model.build()
percept = model.predict_percept(ArgusII(stim=np.ones(60)))
# Most spots are pretty bright, but there are 2 dimmer ones (due to their
# location on the retina):
npt.assert_equal(np.sum(percept.data > 0.5), 28)
npt.assert_equal(np.sum(percept.data > 0.275), 56)
# Model gives same outcome as Spatial:
spatial = AxonMapSpatial(engine='serial',
xystep=1,
rho=100,
axlambda=40,
xrange=(-20, 20),
yrange=(-15, 15),
n_axons=500)
spatial.build()
spatial_percept = spatial.predict_percept(ArgusII(stim=np.ones(60)))
npt.assert_almost_equal(percept.data, spatial_percept.data)
npt.assert_equal(percept.time, None)
# Warning for nonzero electrode-retina distances
implant = ArgusI(stim=np.ones(16), z=10)
msg = ("Nonzero electrode-retina distances do not have any effect on the "
"model output.")
assert_warns_msg(UserWarning, model.predict_percept, msg, implant)
