def test_meshgrid_nd(allclose):
a = [0, 0, 1]
b = [1, 2, 3]
c = [23, 42]
expected = [
np.array(
[
[[0, 0], [0, 0], [0, 0]],
[[0, 0], [0, 0], [0, 0]],
[[1, 1], [1, 1], [1, 1]],
]
),
np.array(
[
[[1, 1], [2, 2], [3, 3]],
[[1, 1], [2, 2], [3, 3]],
[[1, 1], [2, 2], [3, 3]],
]
),
np.array(
[
[[23, 42], [23, 42], [23, 42]],
[[23, 42], [23, 42], [23, 42]],
[[23, 42], [23, 42], [23, 42]],
]
),
]
actual = meshgrid_nd(a, b, c)
assert allclose(expected, actual)
def tuning_curves(ens, sim, inputs=None):
"""Calculates the tuning curves of an ensemble.
That is the neuron responses in dependence of the vector represented by the
ensemble.
For 1-dimensional ensembles, the unpacked return value of this function
can be passed directly to :func:`matplotlib.pyplot.plot`.
Parameters
----------
ens : nengo.Ensemble
Ensemble to calculate the tuning curves of.
sim : nengo.Simulator
Simulator providing information about the built ensemble. (An unbuilt
ensemble does not have tuning curves assigned to it.)
inputs : sequence of ndarray, optional
The inputs at which the tuning curves will be evaluated. For each of
the `D` ensemble dimensions one array of dimensionality `D` is needed.
The output of :func:`numpy.meshgrid` with ``indexing='ij'`` is in the
right format.
Returns
-------
inputs : sequence of ndarray
The passed or auto-generated `inputs`.
activities : ndarray
The activities of the individual neurons given the `inputs`.
For ensembles with 1 dimension, the rows correspond to the `inputs`
and the columns to individual neurons.
For ensembles with > 1 dimension, the first dimension enumerates the
neurons, the remaining dimensions map to `inputs`.
See Also
--------
response_curves
"""
if inputs is None:
inputs = np.linspace(-ens.radius, ens.radius)
if ens.dimensions > 1:
inputs = npext.meshgrid_nd(*(ens.dimensions * [inputs]))
else:
inputs = [inputs]
inputs = np.asarray(inputs).T
flattened = np.reshape(inputs, (-1, ens.dimensions))
flattened /= ens.radius
x = np.dot(flattened, sim.data[ens].encoders.T)
activities = ens.neuron_type.rates(
x, sim.data[ens].gain, sim.data[ens].bias)
activities = np.reshape(activities, inputs[..., 0].shape + (-1,))
return inputs, activities
def test_meshgrid_nd():
a = [0, 0, 1]
b = [1, 2, 3]
c = [23, 42]
expected = [
np.array([[[0, 0], [0, 0], [0, 0]],
[[0, 0], [0, 0], [0, 0]],
[[1, 1], [1, 1], [1, 1]]]),
np.array([[[1, 1], [2, 2], [3, 3]],
[[1, 1], [2, 2], [3, 3]],
[[1, 1], [2, 2], [3, 3]]]),
np.array([[[23, 42], [23, 42], [23, 42]],
[[23, 42], [23, 42], [23, 42]],
[[23, 42], [23, 42], [23, 42]]])]
actual = meshgrid_nd(a, b, c)
assert np.allclose(expected, actual)