def oasis_function(signal, threshold=0.5):
''' apply OASIS function to a single calcium signal and binarize spike train
with threshold '''
if signal.dtype != np.double:
signal = signal.astype(np.double)
_, train = oasisAR1(signal, g=0.95, s_min=.55)
return np.where(train > threshold, 1.0, 0.0)
def AR1(constrained=False):
g = .95
sn = .3
y, c, s = [a[0] for a in gen_data([g], sn, N=1)]
result = constrained_oasisAR1(y, g, sn) if constrained else oasisAR1(y, g, lam=2.4)
result_foopsi = constrained_foopsi(y, [g], sn) if constrained else foopsi(y, [g], lam=2.4)
npt.assert_allclose(np.corrcoef(result[0], result_foopsi[0])[0, 1], 1)
npt.assert_allclose(np.corrcoef(result[1], result_foopsi[1])[0, 1], 1)
npt.assert_allclose(np.corrcoef(result[0], c)[0, 1], 1, .03)
npt.assert_allclose(np.corrcoef(result[1], s)[0, 1], 1, .2)
def generate_spike_train(hparams, filename):
print('processing file {}...'.format(filename))
with open(filename, 'rb') as file:
data = pickle.load(file)
if 'oasis' in data:
print('oasis spike train already existed in {}'.format(filename))
if hparams.overwrite:
print('overwriting...')
else:
return
oasis = np.zeros((data['signals'].shape), dtype=data['signals'].dtype)
for i in tqdm(range(len(data['signals']))):
_, oasis[i] = oasisAR1(data['signals'][i], g=0.95, s_min=.55)
oasis = np.where(oasis > 0.5, 1.0, 0.0)
data['oasis'] = np.array(oasis, dtype=np.float32)
with open(filename, 'wb') as file:
pickle.dump(data, file)
def AR1_model_deconvole(self, trace, smin=0.7):
c, s = oasisAR1(trace, g=np.exp(-1 / (1.6 * 4.85)), s_min=smin)
return (c, s)