def __getitem__(self, idx):
sliced_spikes = self.spikes[idx]
sliced_tuning = self.tuning_curves[idx]
if type(sliced_spikes) == np.ndarray and type(
sliced_tuning) == np.ndarray:
return nept.Neurons(sliced_spikes, sliced_tuning)
else:
return nept.Neurons(np.array([sliced_spikes]),
np.array([sliced_tuning]))
def time_slice(self, t_starts, t_stops):
""" Gets the neuron spikes corresponding to the time slices of
the original between (and including) times t_starts and t_stops. Setting
either parameter to None uses infinite endpoints for the time interval.
Parameters
----------
neurons : nept.Neurons
t_starts : float or list or None
t_stops : float or list or None
Returns
-------
sliced_spikes : list of nept.SpikeTrain
"""
if t_starts is None:
t_starts = [-np.inf]
if t_stops is None:
t_stops = [np.inf]
if isinstance(t_starts, (int, float)):
t_starts = [t_starts]
if isinstance(t_stops, (int, float)):
t_stops = [t_stops]
sliced_spikes = [
spiketrain.time_slice(t_starts, t_stops)
for spiketrain in self.spikes
]
return nept.Neurons(np.array(sliced_spikes), self.tuning_curves)
def test_neurons_get_tuning_shape():
spikes = np.array([
nept.SpikeTrain(np.array([0.5]), 'test'),
nept.SpikeTrain(np.array([1.5]), 'test'),
nept.SpikeTrain(np.array([2.5]), 'test')
])
tuning = np.array([[1., 0., 0., 0.], [0., 1., 0., 0.], [0., 0., 1., 0.]])
neurons = nept.Neurons(spikes, tuning)
assert np.allclose(neurons.tuning_shape, tuning[0].shape)
def test_neurons_basic():
spikes = np.array([
nept.SpikeTrain(np.array([0.5]), 'test'),
nept.SpikeTrain(np.array([1.5]), 'test'),
nept.SpikeTrain(np.array([2.5]), 'test')
])
tuning = np.array([[1., 0., 0., 0.], [0., 1., 0., 0.], [0., 0., 1., 0.]])
neurons = nept.Neurons(spikes, tuning)
assert np.allclose(neurons.spikes[0].time, spikes[0].time)
assert np.allclose(neurons.tuning_curves, tuning)
def test_neurons_getitem_single():
spikes = np.array([
nept.SpikeTrain(np.array([0.5]), 'test'),
nept.SpikeTrain(np.array([1.5]), 'test'),
nept.SpikeTrain(np.array([2.5]), 'test')
])
tuning = np.array([[1., 0., 0., 0.], [0., 1., 0., 0.], [0., 0., 1., 0.]])
neurons = nept.Neurons(spikes, tuning)
sliced = neurons[1]
assert np.allclose(sliced.spikes[0].time, np.array([1.5]))
assert np.allclose(sliced.tuning_curves[0], np.array([0., 1., 0., 0.]))
def test_neurons_n_wrong():
spikes = np.array([
nept.SpikeTrain(np.array([0.5]), 'test'),
nept.SpikeTrain(np.array([1.5]), 'test'),
nept.SpikeTrain(np.array([2.5]), 'test')
])
tuning = np.array([[1., 0., 0., 0.], [0., 1., 0., 0.]])
with pytest.raises(ValueError) as excinfo:
neurons = nept.Neurons(spikes, tuning)
assert str(
excinfo.value
) == 'spikes and tuning curves must have the same number of neurons'
def test_neurons_slicing_specified_stop():
spikes = np.array([
nept.SpikeTrain(np.array([0.5]), 'test'),
nept.SpikeTrain(np.array([1.5]), 'test'),
nept.SpikeTrain(np.array([2.5]), 'test')
])
tuning = np.array([[1., 0., 0., 0.], [0., 1., 0., 0.], [0., 0., 1., 0.]])
neurons = nept.Neurons(spikes, tuning)
t_stop = 2.0
sliced_neurons = neurons.time_slice(None, t_stop)
assert np.allclose(sliced_neurons.spikes[0].time, np.array([0.5]))
assert np.allclose(sliced_neurons.spikes[1].time, np.array([1.5]))
assert np.allclose(sliced_neurons.spikes[2].time, np.array([]))
assert np.allclose(neurons.tuning_curves, tuning)
def test_neurons_slicing_mult():
spikes = np.array([
nept.SpikeTrain(np.array([0.5]), 'test'),
nept.SpikeTrain(np.array([1.5]), 'test'),
nept.SpikeTrain(np.array([2.5]), 'test')
])
tuning = np.array([[1., 0., 0., 0.], [0., 1., 0., 0.], [0., 0., 1., 0.]])
neurons = nept.Neurons(spikes, tuning)
t_starts = [0.0, 2.0]
t_stops = [1.0, 3.0]
sliced_neurons = neurons.time_slice(t_starts, t_stops)
assert np.allclose(sliced_neurons.spikes[0].time, np.array([0.5]))
assert np.allclose(sliced_neurons.spikes[1].time, np.array([]))
assert np.allclose(sliced_neurons.spikes[2].time, np.array([2.5]))
assert np.allclose(neurons.tuning_curves, tuning)
