def test_Model_predict_percept():
# A None Model has nothing to build, nothing to perceive:
model = Model()
npt.assert_equal(model.predict_percept(ArgusI()), None)
npt.assert_equal(model.predict_percept(ArgusI(stim={'A1': 1})), None)
npt.assert_equal(
model.predict_percept(ArgusI(stim={'A1': 1}), t_percept=[0, 1]), None)
# Just the spatial model:
model = Model(spatial=ValidSpatialModel()).build()
npt.assert_equal(model.predict_percept(ArgusI()), None)
# Just the temporal model:
model = Model(temporal=ValidTemporalModel()).build()
npt.assert_equal(model.predict_percept(ArgusI()), None)
# Invalid calls:
model = Model(spatial=ValidSpatialModel(), temporal=ValidTemporalModel())
with pytest.raises(NotBuiltError):
# Must call build first:
model.predict_percept(ArgusI())
model.build()
with pytest.raises(ValueError):
# Cannot request t_percepts that are not multiples of dt:
model.predict_percept(ArgusI(stim=np.ones(16)), t_percept=[0.1, 0.11])
with pytest.raises(ValueError):
# stim.time==None but requesting t_percept != None
model.predict_percept(ArgusI(stim=np.ones(16)), t_percept=[0, 1, 2])
with pytest.raises(TypeError):
# Must pass an implant:
model.predict_percept(Stimulus(3))
def test_Model_build():
# A None model:
model = Model()
# Nothing to build, so `is_built` is always True (we want to be able to
# call `predict_percept`):
npt.assert_equal(model.is_built, True)
model.build()
npt.assert_equal(model.is_built, True)
# SpatialModel, but no TemporalModel:
model = Model(spatial=ValidSpatialModel())
npt.assert_equal(model.is_built, False)
model.build()
npt.assert_equal(model.is_built, True)
# TemporalModel, but no SpatialModel:
model = Model(temporal=ValidTemporalModel())
npt.assert_equal(model.is_built, False)
model.build()
npt.assert_equal(model.is_built, True)
# SpatialModel and TemporalModel:
model = Model(spatial=ValidSpatialModel(), temporal=ValidTemporalModel())
npt.assert_equal(model.is_built, False)
model.build()
npt.assert_equal(model.is_built, True)
def test_Model():
# A None Model:
model = Model()
npt.assert_equal(model.has_space, False)
npt.assert_equal(model.has_time, False)
npt.assert_equal(str(model), "Model(spatial=None, temporal=None)")
# Cannot add attributes outside the constructor:
with pytest.raises(AttributeError):
model.a
with pytest.raises(FreezeError):
model.a = 1
# Wrong model type:
with pytest.raises(TypeError):
Model(spatial=ValidTemporalModel())
with pytest.raises(TypeError):
Model(temporal=ValidSpatialModel())
# SpatialModel, but no TemporalModel:
model = Model(spatial=ValidSpatialModel())
npt.assert_equal(model.has_space, True)
npt.assert_equal(model.has_time, False)
npt.assert_almost_equal(model.xystep, 0.25)
npt.assert_almost_equal(model.spatial.xystep, 0.25)
model.xystep = 2
npt.assert_almost_equal(model.xystep, 2)
npt.assert_almost_equal(model.spatial.xystep, 2)
# Cannot add more attributes:
with pytest.raises(AttributeError):
model.a
with pytest.raises(FreezeError):
model.a = 1
# TemporalModel, but no SpatialModel:
model = Model(temporal=ValidTemporalModel())
npt.assert_equal(model.has_space, False)
npt.assert_equal(model.has_time, True)
npt.assert_almost_equal(model.dt, 5e-3)
npt.assert_almost_equal(model.temporal.dt, 5e-3)
model.dt = 1
npt.assert_almost_equal(model.dt, 1)
npt.assert_almost_equal(model.temporal.dt, 1)
# Cannot add more attributes:
with pytest.raises(AttributeError):
model.a
with pytest.raises(FreezeError):
model.a = 1
# SpatialModel and TemporalModel:
model = Model(spatial=ValidSpatialModel(), temporal=ValidTemporalModel())
npt.assert_equal(model.has_space, True)
npt.assert_equal(model.has_time, True)
npt.assert_almost_equal(model.xystep, 0.25)
npt.assert_almost_equal(model.spatial.xystep, 0.25)
npt.assert_almost_equal(model.dt, 5e-3)
npt.assert_almost_equal(model.temporal.dt, 5e-3)
# Setting a new spatial parameter:
model.xystep = 2
npt.assert_almost_equal(model.xystep, 2)
npt.assert_almost_equal(model.spatial.xystep, 2)
# Setting a new temporal parameter:
model.dt = 1
npt.assert_almost_equal(model.dt, 1)
npt.assert_almost_equal(model.temporal.dt, 1)
# Setting a parameter that's part of both spatial/temporal:
npt.assert_equal(model.thresh_percept, {'spatial': 0, 'temporal': 0})
model.thresh_percept = 1.234
npt.assert_almost_equal(model.spatial.thresh_percept, 1.234)
npt.assert_almost_equal(model.temporal.thresh_percept, 1.234)
# Cannot add more attributes:
with pytest.raises(AttributeError):
model.a
with pytest.raises(FreezeError):
model.a = 1
def test_Model_set_params():
# SpatialModel, but no TemporalModel:
model = Model(spatial=ValidSpatialModel())
model.set_params({'xystep': 2.33})
npt.assert_almost_equal(model.xystep, 2.33)
npt.assert_almost_equal(model.spatial.xystep, 2.33)
# TemporalModel, but no SpatialModel:
model = Model(temporal=ValidTemporalModel())
model.set_params({'dt': 2.33})
npt.assert_almost_equal(model.dt, 2.33)
npt.assert_almost_equal(model.temporal.dt, 2.33)
# SpatialModel and TemporalModel:
model = Model(spatial=ValidSpatialModel(), temporal=ValidTemporalModel())
# Setting both using the convenience function:
model.set_params({'xystep': 5, 'dt': 2.33})
npt.assert_almost_equal(model.xystep, 5)
npt.assert_almost_equal(model.spatial.xystep, 5)
npt.assert_equal(hasattr(model.temporal, 'xystep'), False)
npt.assert_almost_equal(model.dt, 2.33)
npt.assert_almost_equal(model.temporal.dt, 2.33)
npt.assert_equal(hasattr(model.spatial, 'dt'), False)
def test_Model_predict_percept_correctly_parallelizes():
# setup and time spatial model with 1 thread
one_thread_spatial = Model(spatial=ValidSpatialModel(n_threads=1)).build()
start_time_one_thread_spatial = time.perf_counter()
one_thread_spatial.predict_percept(ArgusI())
one_thread_spatial_predict_time = time.perf_counter(
) - start_time_one_thread_spatial
# setup and time spatial model with 2 threads
two_thread_spatial = Model(spatial=ValidSpatialModel(n_threads=2)).build()
start_time_two_thread_spatial = time.perf_counter()
two_thread_spatial.predict_percept(ArgusI())
two_threaded_spatial_predict_time = time.perf_counter(
) - start_time_two_thread_spatial
# we expect roughly a linear decrease in time as thread count increases
npt.assert_almost_equal(actual=two_threaded_spatial_predict_time,
desired=one_thread_spatial_predict_time / 2,
decimal=1e-5)
# setup and time temporal model with 1 thread
one_thread_temporal = Model(temporal=ValidTemporalModel(
n_threads=1)).build()
start_time_one_thread_temporal = time.perf_counter()
one_thread_temporal.predict_percept(ArgusI())
one_thread_temporal_predict_time = time.perf_counter(
) - start_time_one_thread_temporal
# setup and time temporal model with 2 threads
two_thread_temporal = Model(temporal=ValidTemporalModel(
n_threads=2)).build()
start_time_two_thread_temporal = time.perf_counter()
two_thread_temporal.predict_percept(ArgusI())
two_thread_temporal_predict_time = time.perf_counter(
) - start_time_two_thread_temporal
# we expect roughly a linear decrease in time as thread count increases
npt.assert_almost_equal(actual=two_thread_temporal_predict_time,
desired=one_thread_temporal_predict_time / 2,
decimal=1e-5)
