def test_sparseconv():
# time vector for stimulus (long)
maxT = .5 # seconds
nt = 100000
t = np.linspace(0, maxT, nt)
# stimulus (10 Hz anondic and cathodic pulse train)
stim = np.zeros(nt)
stim[0:nt:10000] = 1
stim[100:nt:1000] = -1
# time vector for impulse response (shorter)
tt = t[t < .1]
# impulse reponse (kernel)
G = np.exp(-tt / .005)
# make sure sparseconv returns the same result as np.convolve
# for all modes
modes = ["full", "valid", "same"]
for mode in modes:
# np.convolve
conv = np.convolve(stim, G, mode=mode)
# utils.sparseconv
sparse_conv = utils.sparseconv(stim, G, mode=mode, use_jit=False)
npt.assert_equal(conv.shape, sparse_conv.shape)
npt.assert_almost_equal(conv, sparse_conv)
with pytest.raises(ValueError):
utils.sparseconv(G, stim, mode='invalid')
def test_sparseconv():
# time vector for stimulus (long)
maxT = .5 # seconds
nt = 100000
t = np.linspace(0, maxT, nt)
# stimulus (10 Hz anondic and cathodic pulse train)
stim = np.zeros(nt)
stim[0:nt:10000] = 1
stim[100:nt:1000] = -1
# time vector for impulse response (shorter)
tt = t[t < .1]
# impulse reponse (kernel)
G = np.exp(-tt / .005)
# make sure sparseconv returns the same result as np.convolve
# for all modes
modes = ["full", "valid", "same"]
for mode in modes:
# np.convolve
conv = np.convolve(stim, G, mode=mode)
# utils.sparseconv
sparse_conv = utils.sparseconv(G, stim, mode=mode, dojit=False)
npt.assert_equal(conv.shape, sparse_conv.shape)
npt.assert_almost_equal(conv, sparse_conv)
def fast_response(self, b1, gamma, dojit=True, usefft=False):
"""Fast response function (Box 2) for the bipolar layer
Convolve a stimulus `b1` with a temporal low-pass filter (1-stage
gamma) with time constant `self.tau_inl` ~ 14ms representing bipolars.
Parameters
----------
b1 : array
Temporal signal to process, b1(r,t) in Nanduri et al. (2012).
dojit : bool, optional
If True (default), use numba just-in-time compilation.
usefft : bool, optional
If False (default), use sparseconv, else fftconvolve.
Returns
-------
b2 : array
Fast response, b2(r,t) in Nanduri et al. (2012).
Notes
-----
The function utils.sparseconv can be much faster than np.convolve and
scipy.signals.fftconvolve if `b1` is sparse and much longer than the
convolution kernel.
The output is not converted to a TimeSeries object for speedup.
"""
if usefft: # In Krishnan model, b1 is no longer sparse (run FFT)
conv = self.tsample * fftconvolve(b1, gamma, mode='full')
else:
conv = self.tsample * utils.sparseconv(gamma, b1,
mode='full', dojit=dojit)
# Cut off the tail of the convolution to make the output signal
# match the dimensions of the input signal.
return conv[:b1.shape[-1]]