def test_Nanduri2012Temporal():
model = Nanduri2012Temporal()
# User can set their own params:
model.dt = 0.1
npt.assert_equal(model.dt, 0.1)
model.build(dt=1e-4)
npt.assert_equal(model.dt, 1e-4)
# User cannot add more model parameters:
with pytest.raises(FreezeError):
model.rho = 100
# Nothing in, None out:
npt.assert_equal(model.predict_percept(ArgusI().stim), None)
# Zero in = zero out:
implant = ArgusI(stim=np.zeros((16, 100)))
percept = model.predict_percept(implant.stim, t_percept=[0, 1, 2])
npt.assert_equal(isinstance(percept, Percept), True)
npt.assert_equal(percept.shape, (16, 1, 3))
npt.assert_almost_equal(percept.data, 0)
# Can't request the same time more than once (this would break the Cython
# loop, because `idx_frame` is incremented after a write; also doesn't
# make much sense):
with pytest.raises(ValueError):
implant.stim = np.ones((16, 100))
model.predict_percept(implant.stim, t_percept=[0.2, 0.2])
# Brightness scales differently with amplitude vs frequency:
model = Nanduri2012Temporal(dt=5e-3)
model.build()
sdur = 1000.0 # stimulus duration (ms)
pdur = 0.45 # (ms)
t_percept = np.arange(0, sdur, 5)
implant = ProsthesisSystem(ElectrodeArray(DiskElectrode(0, 0, 0, 260)))
bright_amp = []
for amp in np.linspace(0, 50, 5):
# implant.stim = PulseTrain(model.dt, freq=20, amp=amp, dur=sdur,
# pulse_dur=pdur, interphase_dur=pdur)
implant.stim = BiphasicPulseTrain(20, amp, pdur, interphase_dur=pdur,
stim_dur=sdur)
percept = model.predict_percept(implant.stim, t_percept=t_percept)
bright_amp.append(percept.data.max())
bright_amp_ref = [0.0, 0.00890, 0.0657, 0.1500, 0.1691]
npt.assert_almost_equal(bright_amp, bright_amp_ref, decimal=3)
bright_freq = []
for freq in np.linspace(0, 100, 5):
# implant.stim = PulseTrain(model.dt, freq=freq, amp=20, dur=sdur,
# pulse_dur=pdur, interphase_dur=pdur)
implant.stim = BiphasicPulseTrain(freq, 20, pdur, interphase_dur=pdur,
stim_dur=sdur)
percept = model.predict_percept(implant.stim, t_percept=t_percept)
bright_freq.append(percept.data.max())
bright_freq_ref = [0.0, 0.0394, 0.0741, 0.1073, 0.1385]
npt.assert_almost_equal(bright_freq, bright_freq_ref, decimal=3)
def test_Nanduri2012Spatial():
# Nanduri2012Spatial automatically sets `atten_a`:
model = Nanduri2012Spatial(engine='serial', xystep=5)
# User can set `atten_a`:
model.atten_a = 12345
npt.assert_equal(model.atten_a, 12345)
model.build(atten_a=987)
npt.assert_equal(model.atten_a, 987)
# 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)
# Only works for DiskElectrode arrays:
with pytest.raises(TypeError):
implant = ProsthesisSystem(ElectrodeArray(PointSource(0, 0, 0)))
implant.stim = 1
model.predict_percept(implant)
with pytest.raises(TypeError):
implant = ProsthesisSystem(
ElectrodeArray(
[DiskElectrode(0, 0, 0, 100),
PointSource(100, 100, 0)]))
implant.stim = [1, 1]
model.predict_percept(implant)
# Multiple frames are processed independently:
model = Nanduri2012Spatial(engine='serial',
atten_a=14000,
xystep=5,
xrange=(-20, 20),
yrange=(-15, 15))
model.build()
percept = model.predict_percept(ArgusI(stim={'A1': [1, 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, :], pmax)
npt.assert_almost_equal(pmax[1] / pmax[0], 2.0)
# Nanduri model uses a linear dva2ret conversion factor:
for factor in [0.0, 1.0, 2.0]:
npt.assert_almost_equal(model.retinotopy.dva2ret(factor, factor),
(280.0 * factor, 280.0 * factor))
for factor in [0.0, 1.0, 2.0]:
npt.assert_almost_equal(
model.retinotopy.ret2dva(280.0 * factor, 280.0 * factor),
(factor, factor))
def test_SpatialModel():
# Build grid:
model = ValidSpatialModel()
npt.assert_equal(model.grid, None)
npt.assert_equal(model.is_built, False)
model.build()
npt.assert_equal(model.is_built, True)
npt.assert_equal(isinstance(model.grid, GridXY), True)
npt.assert_equal(isinstance(model.grid.xret, np.ndarray), True)
# Can overwrite default values:
model = ValidSpatialModel(xystep=1.234)
npt.assert_almost_equal(model.xystep, 1.234)
model.build(xystep=2.345)
npt.assert_almost_equal(model.xystep, 2.345)
# Cannot add more attributes:
with pytest.raises(AttributeError):
ValidSpatialModel(newparam=1)
with pytest.raises(FreezeError):
model.newparam = 1
# Returns Percept object of proper size:
npt.assert_equal(model.predict_percept(ArgusI()), None)
for stim in [np.ones(16), np.zeros(16), {'A1': 2}, np.ones((16, 2))]:
implant = ArgusI(stim=stim)
percept = model.predict_percept(implant)
npt.assert_equal(isinstance(percept, Percept), True)
n_time = 1 if implant.stim.time is None else len(implant.stim.time)
npt.assert_equal(
percept.shape,
(model.grid.x.shape[0], model.grid.x.shape[1], n_time))
npt.assert_almost_equal(percept.data, 0)
# Invalid calls:
with pytest.raises(ValueError):
# stim.time==None but requesting t_percept != None
implant.stim = np.ones(16)
model.predict_percept(implant, t_percept=[0, 1, 2])
with pytest.raises(NotBuiltError):
# must call build first
model = ValidSpatialModel()
model.predict_percept(ArgusI())
with pytest.raises(TypeError):
# must pass an implant
ValidSpatialModel().build().predict_percept(Stimulus(3))
def test_Nanduri2012Model_predict_percept():
# Nothing in = nothing out:
model = Nanduri2012Model(xrange=(0, 0), yrange=(0, 0), engine='serial')
model.build()
implant = ArgusI(stim=None)
npt.assert_equal(model.predict_percept(implant), None)
implant.stim = np.zeros(16)
npt.assert_almost_equal(model.predict_percept(implant).data, 0)
# Single-pixel model same as TemporalModel:
implant = ProsthesisSystem(DiskElectrode(0, 0, 0, 100))
# implant.stim = PulseTrain(5e-6)
implant.stim = BiphasicPulseTrain(20, 20, 0.45, interphase_dur=0.45)
t_percept = [0, 0.01, 1.0]
percept = model.predict_percept(implant, t_percept=t_percept)
temp = Nanduri2012Temporal().build()
temp = temp.predict_percept(implant.stim, t_percept=t_percept)
npt.assert_almost_equal(percept.data, temp.data, decimal=4)
# Only works for DiskElectrode arrays:
with pytest.raises(TypeError):
implant = ProsthesisSystem(ElectrodeArray(PointSource(0, 0, 0)))
implant.stim = 1
model.predict_percept(implant)
with pytest.raises(TypeError):
implant = ProsthesisSystem(
ElectrodeArray(
[DiskElectrode(0, 0, 0, 100),
PointSource(100, 100, 0)]))
implant.stim = [1, 1]
model.predict_percept(implant)
# Requested times must be multiples of model.dt:
implant = ProsthesisSystem(ElectrodeArray(DiskElectrode(0, 0, 0, 260)))
# implant.stim = PulseTrain(tsample)
implant.stim = BiphasicPulseTrain(20, 20, 0.45)
model.temporal.dt = 0.1
with pytest.raises(ValueError):
model.predict_percept(implant, t_percept=[0.01])
with pytest.raises(ValueError):
model.predict_percept(implant, t_percept=[0.01, 1.0])
with pytest.raises(ValueError):
model.predict_percept(implant, t_percept=np.arange(0, 0.5, 0.101))
model.predict_percept(implant, t_percept=np.arange(0, 0.5, 1.0000001))
# Can't request the same time more than once (this would break the Cython
# loop, because `idx_frame` is incremented after a write; also doesn't
# make much sense):
with pytest.raises(ValueError):
model.predict_percept(implant, t_percept=[0.2, 0.2])
# It's ok to extrapolate beyond `stim` if the `extrapolate` flag is set:
model.temporal.dt = 1e-2
npt.assert_almost_equal(
model.predict_percept(implant, t_percept=10000).data, 0)
# Output shape must be determined by t_percept:
npt.assert_equal(
model.predict_percept(implant, t_percept=0).shape, (1, 1, 1))
npt.assert_equal(
model.predict_percept(implant, t_percept=[0, 1]).shape, (1, 1, 2))
# Brightness vs. size (use values from Nanduri paper):
model = Nanduri2012Model(xystep=0.5, xrange=(-4, 4), yrange=(-4, 4))
model.build()
implant = ProsthesisSystem(ElectrodeArray(DiskElectrode(0, 0, 0, 260)))
amp_th = 30
bright_th = 0.107
stim_dur = 1000.0
pdur = 0.45
t_percept = np.arange(0, stim_dur, 5)
amp_factors = [1, 6]
frames_amp = []
for amp_f in amp_factors:
implant.stim = BiphasicPulseTrain(20,
amp_f * amp_th,
pdur,
interphase_dur=pdur,
stim_dur=stim_dur)
percept = model.predict_percept(implant, t_percept=t_percept)
idx_frame = np.argmax(np.max(percept.data, axis=(0, 1)))
brightest_frame = percept.data[..., idx_frame]
frames_amp.append(brightest_frame)
npt.assert_equal([np.sum(f > bright_th) for f in frames_amp], [0, 161])
freqs = [20, 120]
frames_freq = []
for freq in freqs:
implant.stim = BiphasicPulseTrain(freq,
1.25 * amp_th,
pdur,
interphase_dur=pdur,
stim_dur=stim_dur)
percept = model.predict_percept(implant, t_percept=t_percept)
idx_frame = np.argmax(np.max(percept.data, axis=(0, 1)))
brightest_frame = percept.data[..., idx_frame]
frames_freq.append(brightest_frame)
npt.assert_equal([np.sum(f > bright_th) for f in frames_freq], [21, 49])
def test_biphasicAxonMapSpatial(engine):
if engine == 'jax' and not has_jax:
pytest.skip("Jax not installed")
# Lambda cannot be too small:
with pytest.raises(ValueError):
BiphasicAxonMapSpatial(axlambda=9).build()
model = BiphasicAxonMapModel(engine=engine, xystep=2).build()
# Only accepts biphasic pulse trains with no delay dur
implant = ArgusI(stim=np.ones(16))
with pytest.raises(TypeError):
model.predict_percept(implant)
# 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)
# Should be equal to axon map model if effects models return 1
model = BiphasicAxonMapSpatial(engine=engine, xystep=2)
def bright_model(freq, amp, pdur):
return 1
def size_model(freq, amp, pdur):
return 1
def streak_model(freq, amp, pdur):
return 1
model.bright_model = bright_model
model.size_model = size_model
model.streak_model = streak_model
model.build()
axon_map = AxonMapSpatial(xystep=2).build()
implant = ArgusII()
implant.stim = Stimulus({'A5': BiphasicPulseTrain(20, 1, 0.45)})
percept = model.predict_percept(implant)
percept_axon = axon_map.predict_percept(implant)
npt.assert_almost_equal(percept.data[:, :, 0],
percept_axon.max(axis='frames'))
# Effect models must be callable
model = BiphasicAxonMapSpatial(engine=engine, xystep=2)
model.bright_model = 1.0
with pytest.raises(TypeError):
model.build()
# If t_percept is not specified, there should only be one frame
model = BiphasicAxonMapSpatial(engine=engine, xystep=2)
model.build()
implant = ArgusII()
implant.stim = Stimulus({'A5': BiphasicPulseTrain(20, 1, 0.45)})
percept = model.predict_percept(implant)
npt.assert_equal(percept.time is None, True)
# If t_percept is specified, only first frame should have data
# and the rest should be empty
percept = model.predict_percept(implant, t_percept=[0, 1, 2, 5, 10])
npt.assert_equal(len(percept.time), 5)
npt.assert_equal(np.any(percept.data[:, :, 0]), True)
npt.assert_equal(np.any(percept.data[:, :, 1:]), False)
# Test that default models give expected values
model = BiphasicAxonMapSpatial(engine=engine,
rho=400,
axlambda=600,
xystep=1,
xrange=(-20, 20),
yrange=(-15, 15))
model.build()
implant = ArgusII()
implant.stim = Stimulus({'A4': BiphasicPulseTrain(20, 1, 1)})
percept = model.predict_percept(implant)
npt.assert_equal(np.sum(percept.data > 0.0813), 69)
npt.assert_equal(np.sum(percept.data > 0.1626), 46)
npt.assert_equal(np.sum(percept.data > 0.2439), 32)
npt.assert_equal(np.sum(percept.data > 0.4065), 14)
npt.assert_equal(np.sum(percept.data > 0.5691), 4)
