def test_tuning_curve_1d_with_2d_position():
position = nept.Position(np.hstack([np.array([2, 4, 6, 8])[..., np.newaxis],
np.array([7, 5, 3, 1])[..., np.newaxis]]),
np.array([0., 1., 2., 3.]))
binsize = 2
spikes = [nept.SpikeTrain(np.array([0., 3., 3., 3.]), 'test')]
with pytest.raises(ValueError) as excinfo:
nept.tuning_curve_1d(position, spikes, binsize=binsize)
assert str(excinfo.value) == 'position must be linear'
def test_tuning_curve_1d_with_2d_position():
position = nept.Position(
np.hstack([
np.array([2, 4, 6, 8])[..., np.newaxis],
np.array([7, 5, 3, 1])[..., np.newaxis]
]), np.array([0., 1., 2., 3.]))
binsize = 2
spikes = [nept.SpikeTrain(np.array([0., 3., 3., 3.]), 'test')]
with pytest.raises(ValueError) as excinfo:
nept.tuning_curve_1d(position, spikes, binsize=binsize)
assert str(excinfo.value) == 'position must be linear'
def test_simple_tc():
linear = nept.Position(np.linspace(0, 10, 4), np.linspace(0, 3, 4))
spikes = [nept.SpikeTrain(np.array([0.5]), 'test'),
nept.SpikeTrain(np.array([1.5]), 'test'),
nept.SpikeTrain(np.array([2.5]), 'test')]
tuning = nept.tuning_curve_1d(linear, spikes, binsize=3, gaussian_std=None)
assert np.allclose(tuning, ([1., 0., 0., 0.], [0., 1., 0., 0.], [0., 0., 1., 0.]))
def test_sort_idx():
linear = nept.Position(np.linspace(0, 10, 4), np.linspace(0, 3, 4))
spikes = [nept.SpikeTrain(np.array([1.5]), 'test'),
nept.SpikeTrain(np.array([0.5]), 'test'),
nept.SpikeTrain(np.array([2.5]), 'test')]
tuning = nept.tuning_curve_1d(linear, spikes, binsize=3, gaussian_std=None)
sort_idx = nept.get_sort_idx(tuning)
assert np.allclose(sort_idx, [1, 0, 2])
def test_simple_tc1():
"""Time spent in each bin not the same."""
linear = nept.Position(np.linspace(0, 9, 4), np.linspace(0, 3, 4))
spikes = [nept.SpikeTrain(np.array([0.0]), 'test'),
nept.SpikeTrain(np.array([1.0]), 'test'),
nept.SpikeTrain(np.array([2.0]), 'test'),
nept.SpikeTrain(np.array([2.5]), 'test')]
tuning = nept.tuning_curve_1d(linear, spikes, binsize=3, gaussian_std=None)
assert np.allclose(tuning, ([1., 0., 0.], [0., 1., 0.], [0., 0., 0.5], [0., 0., 0.5]))
def test_simple_tc():
linear = nept.Position(np.linspace(0, 10, 4), np.linspace(0, 3, 4))
spikes = [
nept.SpikeTrain(np.array([0.5]), 'test'),
nept.SpikeTrain(np.array([1.5]), 'test'),
nept.SpikeTrain(np.array([2.5]), 'test')
]
tuning = nept.tuning_curve_1d(linear, spikes, binsize=3, gaussian_std=None)
assert np.allclose(tuning,
([1., 0., 0., 0.], [0., 1., 0., 0.], [0., 0., 1., 0.]))
def test_sort_idx():
linear = nept.Position(np.linspace(0, 10, 4), np.linspace(0, 3, 4))
spikes = [
nept.SpikeTrain(np.array([1.5]), 'test'),
nept.SpikeTrain(np.array([0.5]), 'test'),
nept.SpikeTrain(np.array([2.5]), 'test')
]
tuning = nept.tuning_curve_1d(linear, spikes, binsize=3, gaussian_std=None)
sort_idx = nept.get_sort_idx(tuning)
assert np.allclose(sort_idx, [1, 0, 2])
def test_tuning_curve_1d_gaussian():
linear = nept.Position(np.linspace(0, 9, 4), np.linspace(0, 3, 4))
spikes = [nept.SpikeTrain(np.array([0.0]), 'test'),
nept.SpikeTrain(np.array([1.0]), 'test'),
nept.SpikeTrain(np.array([2.0]), 'test'),
nept.SpikeTrain(np.array([2.5]), 'test')]
tuning = nept.tuning_curve_1d(linear, spikes, binsize=3, gaussian_std=1.5)
assert np.allclose(tuning, ([0.78698604, 0.10650698, 0.0],
[0.10650698, 0.78698604, 0.10650698],
[0.0, 0.05325349, 0.39349302],
[0.0, 0.05325349, 0.39349302]))
def test_tuning_curve_1d_gaussian():
linear = nept.Position(np.linspace(0, 9, 4), np.linspace(0, 3, 4))
spikes = [
nept.SpikeTrain(np.array([0.0]), 'test'),
nept.SpikeTrain(np.array([1.0]), 'test'),
nept.SpikeTrain(np.array([2.0]), 'test'),
nept.SpikeTrain(np.array([2.5]), 'test')
]
tuning = nept.tuning_curve_1d(linear, spikes, binsize=3, gaussian_std=1.5)
assert np.allclose(tuning, ([0.78698604, 0.10650698, 0.0], [
0.10650698, 0.78698604, 0.10650698
], [0.0, 0.05325349, 0.39349302], [0.0, 0.05325349, 0.39349302]))
def test_simple_tc1():
"""Time spent in each bin not the same."""
linear = nept.Position(np.linspace(0, 9, 4), np.linspace(0, 3, 4))
spikes = [
nept.SpikeTrain(np.array([0.0]), 'test'),
nept.SpikeTrain(np.array([1.0]), 'test'),
nept.SpikeTrain(np.array([2.0]), 'test'),
nept.SpikeTrain(np.array([2.5]), 'test')
]
tuning = nept.tuning_curve_1d(linear, spikes, binsize=3, gaussian_std=None)
assert np.allclose(
tuning, ([1., 0., 0.], [0., 1., 0.], [0., 0., 0.5], [0., 0., 0.5]))