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""" directionality_python_backend.py
Collection of python functions that can be used instead of the cython
implementation.
Copyright 2015, Mario Mulansky <mario.mulansky@gmx.net>
Distributed under the BSD License
"""
import numpy as np
from pyspike.cython.python_backend import get_tau
############################################################
# spike_train_order_python
############################################################
def spike_directionality_profile_python(spikes1, spikes2, t_start, t_end,
max_tau, MRTS=0.):
true_max = t_end - t_start
if max_tau > 0:
true_max = min(true_max, 2*max_tau)
N1 = len(spikes1)
N2 = len(spikes2)
i = -1
j = -1
d1 = np.zeros(N1) # directionality values
d2 = np.zeros(N2) # directionality values
while i + j < N1 + N2 - 2:
if (i < N1-1) and (j == N2-1 or spikes1[i+1] < spikes2[j+1]):
i += 1
tau = get_tau(spikes1, spikes2, i, j, true_max, MRTS)
if j > -1 and spikes1[i]-spikes2[j] < tau:
# coincidence between the current spike and the previous spike
# spike in first spike train occurs after second
d1[i] = -1
d2[j] = +1
elif (j < N2-1) and (i == N1-1 or spikes1[i+1] > spikes2[j+1]):
j += 1
tau = get_tau(spikes1, spikes2, i, j, true_max, MRTS)
if i > -1 and spikes2[j]-spikes1[i] < tau:
# coincidence between the current spike and the previous spike
# spike in second spike train occurs after first
d1[i] = +1
d2[j] = -1
else: # spikes1[i+1] = spikes2[j+1]
# advance in both spike trains
j += 1
i += 1
d1[i] = 0
d2[j] = 0
return d1, d2
############################################################
# spike_train_order_python
############################################################
def spike_train_order_profile_python(spikes1, spikes2, t_start, t_end,
max_tau, MRTS=0.):
true_max = t_end - t_start
if max_tau > 0:
true_max = min(true_max, 2*max_tau)
N1 = len(spikes1)
N2 = len(spikes2)
i = -1
j = -1
n = 0
st = np.zeros(N1 + N2 + 2) # spike times
a = np.zeros(N1 + N2 + 2) # coincidences
mp = np.ones(N1 + N2 + 2) # multiplicity
while i + j < N1 + N2 - 2:
if (i < N1-1) and (j == N2-1 or spikes1[i+1] < spikes2[j+1]):
i += 1
n += 1
tau = get_tau(spikes1, spikes2, i, j, true_max, MRTS)
st[n] = spikes1[i]
if j > -1 and spikes1[i]-spikes2[j] < tau:
# coincidence between the current spike and the previous spike
# both get marked with 1
a[n] = -1
a[n-1] = -1
elif (j < N2-1) and (i == N1-1 or spikes1[i+1] > spikes2[j+1]):
j += 1
n += 1
tau = get_tau(spikes1, spikes2, i, j, true_max, MRTS)
st[n] = spikes2[j]
if i > -1 and spikes2[j]-spikes1[i] < tau:
# coincidence between the current spike and the previous spike
# both get marked with 1
a[n] = 1
a[n-1] = 1
else: # spikes1[i+1] = spikes2[j+1]
# advance in both spike trains
j += 1
i += 1
n += 1
# add only one event with zero asymmetry value and multiplicity 2
st[n] = spikes1[i]
a[n] = 0
mp[n] = 2
st = st[:n+2]
a = a[:n+2]
mp = mp[:n+2]
st[0] = t_start
st[len(st)-1] = t_end
if N1 + N2 > 0:
a[0] = a[1]
a[len(a)-1] = a[len(a)-2]
mp[0] = mp[1]
mp[len(mp)-1] = mp[len(mp)-2]
else:
a[0] = 1
a[1] = 1
return st, a, mp
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