def test_deprecated():
assert_warns_msg(DeprecationWarning, MockClass1, 'Use ``qwerty`` instead')
assert_warns_msg(DeprecationWarning,
MockClass2().mymethod,
'since version 0.1, and will be removed in version 0.2')
assert_warns_msg(DeprecationWarning, MockClass3, 'deprecated')
assert_warns_msg(DeprecationWarning, mock_function, 'since version 0.4')
def test_Stimulus__stim():
stim = Stimulus(3)
# User could try and motify the data container after the constructor, which
# would lead to inconsistencies between data, electrodes, time. The new
# property setting mechanism prevents that.
# Requires dict:
with pytest.raises(AttributeError):
stim._stim = np.array([0, 1])
# Dict must have all required fields:
fields = ['data', 'electrodes', 'time']
for field in fields:
_fields = deepcopy(fields)
_fields.remove(field)
with pytest.raises(AttributeError):
stim._stim = {f: None for f in _fields}
# Data must be a 2-D NumPy array:
data = {f: None for f in fields}
with pytest.raises(ValueError):
data['data'] = np.ones(3)
stim._stim = data
# Data rows must match electrodes:
with pytest.raises(ValueError):
data['data'] = np.ones((3, 4))
data['time'] = np.arange(4)
data['electrodes'] = np.arange(2)
stim._stim = data
# Data columns must match time:
with pytest.raises(ValueError):
data['data'] = np.ones((3, 4))
data['electrodes'] = np.arange(3)
data['time'] = np.arange(7)
stim._stim = data
# Time points must be unique:
assert_warns_msg(UserWarning, _unique_timepoints, None, stim, data)
# But if you do all the things right, you can reset the stimulus by hand:
data['data'] = np.ones((3, 1))
data['electrodes'] = np.arange(3)
data['time'] = None
stim._stim = data
data['data'] = np.ones((3, 1))
data['electrodes'] = np.arange(3)
data['time'] = np.arange(1)
stim._stim = data
data['data'] = np.ones((3, 4))
data['electrodes'] = np.arange(3)
data['time'] = np.array([0, 1, 1 + DT, 2])
stim._stim = data
def test_ScoreboardModel_predict_percept():
model = ScoreboardModel(xystep=0.55,
rho=100,
thresh_percept=0,
xrange=(-20, 20),
yrange=(-15, 15))
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, very small Gaussian kernel:
npt.assert_equal(np.sum(percept.data > 0.8), 1)
npt.assert_equal(np.sum(percept.data > 0.5), 2)
npt.assert_equal(np.sum(percept.data > 0.1), 7)
npt.assert_equal(np.sum(percept.data > 0.00001), 32)
# Brightest pixel is in lower right:
npt.assert_almost_equal(percept.data[33, 46, 0], np.max(percept.data))
# Full Argus II: 60 bright spots
model = ScoreboardModel(engine='serial', xystep=0.55, rho=100)
model.build()
percept = model.predict_percept(ArgusII(stim=np.ones(60)))
npt.assert_equal(np.sum(np.isclose(percept.data, 0.8, rtol=0.1, atol=0.1)),
88)
# Model gives same outcome as Spatial:
spatial = ScoreboardSpatial(engine='serial', xystep=1, rho=100)
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)
# 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)
def test_assert_warns_msg():
for warning in [UserWarning, DeprecationWarning]:
assert_warns_msg(warning, _mock_warns, str(warning), category=warning)
def test_Stimulus():
# Slots:
npt.assert_equal(hasattr(Stimulus(1), '__slots__'), True)
npt.assert_equal(hasattr(Stimulus(1), '__dict__'), False)
# One electrode:
stim = Stimulus(3)
npt.assert_equal(stim.shape, (1, 1))
npt.assert_equal(stim.electrodes, [0])
npt.assert_equal(stim.time, None)
# One electrode with a name:
stim = Stimulus(3, electrodes='AA001')
npt.assert_equal(stim.shape, (1, 1))
npt.assert_equal(stim.electrodes, ['AA001'])
npt.assert_equal(stim.time, None)
# Ten electrodes, one will be trimmed:
stim = Stimulus(np.arange(10), compress=True)
npt.assert_equal(stim.shape, (9, 1))
npt.assert_equal(stim.electrodes, np.arange(1, 10))
npt.assert_equal(stim.time, None)
# Electrodes + specific time, time will be trimmed:
stim = Stimulus(np.ones((4, 3)), time=[-3, -2, -1], compress=True)
npt.assert_equal(stim.shape, (4, 2))
npt.assert_equal(stim.time, [-3, -1])
# Electrodes + specific time, but don't trim:
stim = Stimulus(np.ones((4, 3)), time=[-3, -2, -1], compress=False)
npt.assert_equal(stim.shape, (4, 3))
npt.assert_equal(stim.time, [-3, -2, -1])
# Specific names:
stim = Stimulus({'A1': 3, 'C5': 8})
npt.assert_equal(stim.shape, (2, 1))
npt.assert_equal(np.sort(stim.electrodes), np.sort(['A1', 'C5']))
npt.assert_equal(stim.time, None)
# Specific names, renamed:
stim = Stimulus({'A1': 3, 'C5': 8}, electrodes=['B7', 'B8'])
npt.assert_equal(stim.shape, (2, 1))
npt.assert_equal(np.sort(stim.electrodes), np.sort(['B7', 'B8']))
npt.assert_equal(stim.time, None)
# Electrodes x time, time will be trimmed:
stim = Stimulus(np.ones((6, 100)), compress=True)
npt.assert_equal(stim.shape, (6, 2))
# Single electrode in time:
stim = Stimulus([[1, 5, 7, 2, 4]])
npt.assert_equal(stim.electrodes, [0])
npt.assert_equal(stim.shape, (1, 5))
# Specific electrode in time:
stim = Stimulus({'C3': [[1, 4, 4, 3, 6]]})
npt.assert_equal(stim.electrodes, ['C3'])
npt.assert_equal(stim.shape, (1, 5))
# Multiple specific electrodes in time:
stim = Stimulus({'C3': [[0, 1, 2, 3]], 'F4': [[4, -1, 4, -1]]})
# Stimulus from a Stimulus (might happen in ProsthesisSystem):
stim = Stimulus(Stimulus(4), electrodes='B3')
npt.assert_equal(stim.shape, (1, 1))
npt.assert_equal(stim.electrodes, ['B3'])
npt.assert_equal(stim.time, None)
# Saves metadata:
metadata = {'a': 0, 'b': 1}
stim = Stimulus(3, metadata=metadata)
npt.assert_equal(stim.metadata['user'], metadata)
# List of lists instead of 2D NumPy array:
stim = Stimulus([[1, 1, 1, 1, 1], [1, 1, 1, 1, 1]], compress=True)
npt.assert_equal(stim.shape, (2, 2))
npt.assert_equal(stim.electrodes, [0, 1])
npt.assert_equal(stim.time, [0, 4])
# Tuple of tuples instead of 2D NumPy array:
stim = Stimulus(((1, 1, 1, 1, 1), (1, 1, 1, 1, 1)), compress=True)
npt.assert_equal(stim.shape, (2, 2))
npt.assert_equal(stim.electrodes, [0, 1])
npt.assert_equal(stim.time, [0, 4])
# Zero activation:
source = np.zeros((2, 4))
stim = Stimulus(source, compress=True)
npt.assert_equal(stim.shape, (0, 2))
npt.assert_equal(stim.time, [0, source.shape[1] - 1])
stim = Stimulus(source, compress=False)
npt.assert_equal(stim.shape, source.shape)
npt.assert_equal(stim.time, np.arange(source.shape[1]))
# Annoying but possible:
stim = Stimulus([])
npt.assert_equal(stim.time, None)
npt.assert_equal(len(stim.data), 0)
npt.assert_equal(len(stim.electrodes), 0)
npt.assert_equal(stim.shape, (0, ))
# Rename electrodes:
stim = Stimulus(np.ones((2, 5)), compress=True)
npt.assert_equal(stim.electrodes, [0, 1])
stim = Stimulus(stim, electrodes=['A3', 'B8'])
npt.assert_equal(stim.electrodes, ['A3', 'B8'])
npt.assert_equal(stim.time, [0, 4])
# Individual stimuli might already have electrode names:
stim = Stimulus([Stimulus(1, electrodes='B1')])
npt.assert_equal(stim.electrodes, ['B1'])
# Duplicate names will be fixed (with a warning message):
stim = Stimulus([Stimulus(1), Stimulus(2)])
npt.assert_equal(stim.electrodes, [0, 1])
# When passing a dict and the stimuli already have electrode names, the
# keys of the dict prevail:
stim = Stimulus({'A1': Stimulus(1, electrodes='B2')})
npt.assert_equal(stim.electrodes, ['A1'])
# Specify new time points:
stim = Stimulus(np.ones((2, 5)), compress=True)
npt.assert_equal(stim.time, [0, 4])
stim = Stimulus(stim, time=np.array(stim.time) / 10.0)
npt.assert_equal(stim.electrodes, [0, 1])
npt.assert_almost_equal(stim.time, [0, 0.4])
# Charge-balanced:
npt.assert_equal(Stimulus(0).is_charge_balanced, True)
npt.assert_equal(Stimulus(1).is_charge_balanced, False)
npt.assert_equal(Stimulus([0, 0]).is_charge_balanced, True)
npt.assert_equal(Stimulus([[0, 0]]).is_charge_balanced, True)
npt.assert_equal(Stimulus([1, -1]).is_charge_balanced, False)
npt.assert_equal(Stimulus([[1, -1]]).is_charge_balanced, True)
npt.assert_equal(Stimulus([[1, -1], [0, 0.5]]).is_charge_balanced, False)
# Not allowed:
with pytest.raises(ValueError):
# First one doesn't have time:
stim = Stimulus({'A2': 1, 'C3': [[1, 2, 3]]})
with pytest.raises(ValueError):
# Invalid source type:
stim = Stimulus(np.ones((3, 4, 5, 6)))
with pytest.raises(TypeError):
# Invalid source type:
stim = Stimulus("invalid")
with pytest.raises(ValueError):
# Wrong number of electrodes:
stim = Stimulus([3, 4], electrodes='A1')
with pytest.raises(ValueError):
# Wrong number of time points:
stim = Stimulus(np.ones((3, 5)), time=[0, 1, 2])
with pytest.raises(ValueError):
# Can't force time:
stim = Stimulus(3, time=[0.4])
assert_warns_msg(UserWarning,
Stimulus,
None, [[1, 2, 3]],
time=[1, 2, 1.9])
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)