def _insert(self, delay, target):
'''
Vectorised insertion of spike events.
Parameters
----------
delay : ndarray
Delays in timesteps.
target : ndarray
Target synaptic indices.
'''
delay = np.array(delay, dtype=int)
offset = calc_repeats(delay)
# Calculate row indices in the data structure
timesteps = (self.currenttime + delay) % len(self.n)
# (Over)estimate the number of events to be stored, to resize the array
# It's an overestimation for the current time, but I believe a good one
# for future events
m = max(self.n) + len(target)
if (m >= self.X.shape[1]): # overflow
self._resize(m+1)
self.X_flat[timesteps*self.X.shape[1]+offset+self.n[timesteps]] = target
self.n[timesteps] += offset+1 # that's a trick (to update stack size)
def _insert(self, delay, target):
'''
Vectorised insertion of spike events.
Parameters
----------
delay : ndarray
Delays in timesteps.
target : ndarray
Target synaptic indices.
'''
delay = np.array(delay, dtype=int)
offset = calc_repeats(delay)
# Calculate row indices in the data structure
timesteps = (self.currenttime + delay) % len(self.n)
# (Over)estimate the number of events to be stored, to resize the array
# It's an overestimation for the current time, but I believe a good one
# for future events
m = max(self.n) + len(target)
if (m >= self.X.shape[1]): # overflow
self._resize(m + 1)
self.X_flat[timesteps * self.X.shape[1] + offset +
self.n[timesteps]] = target
self.n[
timesteps] += offset + 1 # that's a trick (to update stack size)