def __init__(self, **kwargs):
# Set default values of keyword arguments
self.tau_gcl = 0.42 / 1000
self.tau_inl = 18.0 / 1000
self.tau_ca = 45.25 / 1000
self.tau_slow = 26.25 / 1000
self.scale_ca = 42.1
self.scale_slow = 1150.0
self.lweight = 0.636
self.aweight = 0.5
self.slope = 3.0
self.shift = 15.0
# Overwrite any given keyword arguments, print warning message (True)
# if attempting to set an unrecognized keyword
self.set_kwargs(True, **kwargs)
# perform one-time setup calculations
_, self.gamma_inl = utils.gamma(1, self.tau_inl, self.tsample)
_, self.gamma_gcl = utils.gamma(1, self.tau_gcl, self.tsample)
# gamma_ca is used to calculate charge accumulation
_, self.gamma_ca = utils.gamma(1, self.tau_ca, self.tsample)
# gamma_slow is used to calculate the slow response
_, self.gamma_slow = utils.gamma(3, self.tau_slow, self.tsample)
def __init__(self, **kwargs):
self.tsample = 0.01 / 1000
self.tau1 = 0.42 / 1000
self.tau2 = 45.25 / 1000
self.tau3 = 26.25 / 1000
self.epsilon = 2.25
self.beta = 3.43
# Overwrite any given keyword arguments, print warning message (True)
# if attempting to set an unrecognized keyword
self.set_kwargs(True, **kwargs)
_, self.gamma1 = utils.gamma(1, self.tau1, self.tsample)
_, self.gamma2 = utils.gamma(1, self.tau2, self.tsample)
_, self.gamma3 = utils.gamma(3, self.tau3, self.tsample)
def test_conv(mode, method):
reload(convolution)
# time vector for stimulus (long)
stim_dur = 0.5 # seconds
tsample = 0.001 / 1000
t = np.arange(0, stim_dur, tsample)
# stimulus (10 Hz anondic and cathodic pulse train)
stim = np.zeros_like(t)
stim[::1000] = 1
stim[100::1000] = -1
# kernel
_, gg = gamma(1, 0.005, tsample)
# make sure conv returns the same result as np.convolve for all modes:
npconv = np.convolve(stim, gg, mode=mode)
conv = convolution.conv(stim, gg, mode=mode, method=method)
npt.assert_equal(conv.shape, npconv.shape)
npt.assert_almost_equal(conv, npconv)
with pytest.raises(ValueError):
convolution.conv(gg, stim, mode="invalid")
with pytest.raises(ValueError):
convolution.conv(gg, stim, method="invalid")
def test_conv(mode, method, use_jit):
reload(convolution)
# time vector for stimulus (long)
stim_dur = 0.5 # seconds
tsample = 0.001 / 1000
t = np.arange(0, stim_dur, tsample)
# stimulus (10 Hz anondic and cathodic pulse train)
stim = np.zeros_like(t)
stim[::1000] = 1
stim[100::1000] = -1
# kernel
_, gg = gamma(1, 0.005, tsample)
# make sure conv returns the same result as np.convolve for all modes:
npconv = np.convolve(stim, gg, mode=mode)
conv = convolution.conv(stim, gg, mode=mode, method=method,
use_jit=use_jit)
npt.assert_equal(conv.shape, npconv.shape)
npt.assert_almost_equal(conv, npconv)
with pytest.raises(ValueError):
convolution.conv(gg, stim, mode="invalid", use_jit=use_jit)
with pytest.raises(ValueError):
convolution.conv(gg, stim, method="invalid", use_jit=use_jit)
with mock.patch.dict("sys.modules", {"numba": {}}):
with pytest.raises(ImportError):
reload(convolution)
convolution.conv(stim, gg, mode='full', method='sparse',
use_jit=True)
def __init__(self, **kwargs):
# Set default values of keyword arguments
self.tau1 = 0.42 / 1000
self.tau2 = 45.25 / 1000
self.tau3 = 26.25 / 1000
self.eps = 8.73
self.asymptote = 14.0
self.slope = 3.0
self.shift = 16.0
# Overwrite any given keyword arguments, print warning message (True)
# if attempting to set an unrecognized keyword
self.set_kwargs(True, **kwargs)
# perform one-time setup calculations
# gamma1 is used for the fast response
_, self.gamma1 = utils.gamma(1, self.tau1, self.tsample)
# gamma2 is used to calculate charge accumulation
_, self.gamma2 = utils.gamma(1, self.tau2, self.tsample)
# gamma3 is used to calculate the slow response
_, self.gamma3 = utils.gamma(3, self.tau3, self.tsample)
def test_gamma():
tsample = 0.005 / 1000
with pytest.raises(ValueError):
t, g = gamma(0, 0.1, tsample)
with pytest.raises(ValueError):
t, g = gamma(2, -0.1, tsample)
with pytest.raises(ValueError):
t, g = gamma(2, 0.1, -tsample)
for tau in [0.001, 0.01, 0.1]:
for n in [1, 2, 5]:
t, g = gamma(n, tau, tsample)
npt.assert_equal(np.arange(0, t[-1] + tsample / 2.0, tsample), t)
if n > 1:
npt.assert_equal(g[0], 0.0)
# Make sure area under the curve is normalized
npt.assert_almost_equal(np.trapz(np.abs(g), dx=tsample), 1.0,
decimal=2)
# Make sure peak sits correctly
npt.assert_almost_equal(g.argmax() * tsample, tau * (n - 1))
def test_conv():
reload(utils)
# time vector for stimulus (long)
stim_dur = 0.5 # seconds
tsample = 0.001 / 1000
t = np.arange(0, stim_dur, tsample)
# stimulus (10 Hz anondic and cathodic pulse train)
stim = np.zeros_like(t)
stim[::1000] = 1
stim[100::1000] = -1
# kernel
_, gg = utils.gamma(1, 0.005, tsample)
# make sure conv returns the same result as
# make sure sparseconv returns the same result as np.convolve
# for all modes
methods = ["fft", "sparse"]
modes = ["full", "valid", "same"]
for mode in modes:
# np.convolve
npconv = np.convolve(stim, gg, mode=mode)
for method in methods:
conv = utils.conv(stim, gg, mode=mode, method=method)
npt.assert_equal(conv.shape, npconv.shape)
npt.assert_almost_equal(conv, npconv)
with pytest.raises(ValueError):
utils.conv(gg, stim, mode="invalid")
with pytest.raises(ValueError):
utils.conv(gg, stim, method="invalid")
with mock.patch.dict("sys.modules", {"numba": {}}):
with pytest.raises(ImportError):
reload(utils)
utils.conv(stim, gg, mode='full', method='sparse', use_jit=True)
