diff options
Diffstat (limited to 'pyspike/cython/python_backend.py')
| -rw-r--r-- | pyspike/cython/python_backend.py | 329 |
1 files changed, 210 insertions, 119 deletions
diff --git a/pyspike/cython/python_backend.py b/pyspike/cython/python_backend.py index 749507a..1fd8c42 100644 --- a/pyspike/cython/python_backend.py +++ b/pyspike/cython/python_backend.py @@ -15,50 +15,78 @@ import numpy as np ############################################################ # isi_distance_python ############################################################ -def isi_distance_python(s1, s2): +def isi_distance_python(s1, s2, t_start, t_end): """ Plain Python implementation of the isi distance. """ - # compute the interspike interval - nu1 = s1[1:] - s1[:-1] - nu2 = s2[1:] - s2[:-1] + N1 = len(s1) + N2 = len(s2) # compute the isi-distance - spike_events = np.empty(len(nu1) + len(nu2)) - spike_events[0] = s1[0] + spike_events = np.empty(N1+N2+2) + spike_events[0] = t_start # the values have one entry less - the number of intervals between events isi_values = np.empty(len(spike_events) - 1) - # add the distance of the first events - # isi_values[0] = nu1[0]/nu2[0] - 1.0 if nu1[0] <= nu2[0] \ - # else 1.0 - nu2[0]/nu1[0] - isi_values[0] = abs(nu1[0] - nu2[0]) / max(nu1[0], nu2[0]) - index1 = 0 - index2 = 0 + if s1[0] > t_start: + # edge correction + nu1 = max(s1[0] - t_start, s1[1] - s1[0]) + index1 = -1 + else: + nu1 = s1[1] - s1[0] + index1 = 0 + if s2[0] > t_start: + # edge correction + nu2 = max(s2[0] - t_start, s2[1] - s2[0]) + index2 = -1 + else: + nu2 = s2[1] - s2[0] + index2 = 0 + + isi_values[0] = abs(nu1 - nu2) / max(nu1, nu2) index = 1 - while True: + while index1+index2 < N1+N2-2: # check which spike is next - from s1 or s2 - if s1[index1+1] < s2[index2+1]: + if (index1 < N1-1) and (index2 == N2-1 or s1[index1+1] < s2[index2+1]): index1 += 1 - # break condition relies on existence of spikes at T_end - if index1 >= len(nu1): - break spike_events[index] = s1[index1] - elif s1[index1+1] > s2[index2+1]: + if index1 < N1-1: + nu1 = s1[index1+1]-s1[index1] + else: + # edge correction + nu1 = max(t_end-s1[N1-1], s1[N1-1]-s1[N1-2]) + + elif (index2 < N2-1) and (index1 == N1-1 or + s1[index1+1] > s2[index2+1]): index2 += 1 - if index2 >= len(nu2): - break spike_events[index] = s2[index2] + if index2 < N2-1: + nu2 = s2[index2+1]-s2[index2] + else: + # edge correction + nu2 = max(t_end-s2[N2-1], s2[N2-1]-s2[N2-2]) + else: # s1[index1 + 1] == s2[index2 + 1] index1 += 1 index2 += 1 - if (index1 >= len(nu1)) or (index2 >= len(nu2)): - break spike_events[index] = s1[index1] + if index1 < N1-1: + nu1 = s1[index1+1]-s1[index1] + else: + # edge correction + nu1 = max(t_end-s1[N1-1], s1[N1-1]-s1[N1-2]) + if index2 < N2-1: + nu2 = s2[index2+1]-s2[index2] + else: + # edge correction + nu2 = max(t_end-s2[N2-1], s2[N2-1]-s2[N2-2]) # compute the corresponding isi-distance - isi_values[index] = abs(nu1[index1] - nu2[index2]) / \ - max(nu1[index1], nu2[index2]) + isi_values[index] = abs(nu1 - nu2) / \ + max(nu1, nu2) index += 1 # the last event is the interval end - spike_events[index] = s1[-1] + if spike_events[index-1] == t_end: + index -= 1 + else: + spike_events[index] = t_end # use only the data added above # could be less than original length due to equal spike times return spike_events[:index + 1], isi_values[:index] @@ -67,122 +95,186 @@ def isi_distance_python(s1, s2): ############################################################ # get_min_dist ############################################################ -def get_min_dist(spike_time, spike_train, start_index=0): +def get_min_dist(spike_time, spike_train, start_index, t_start, t_end): """ Returns the minimal distance |spike_time - spike_train[i]| with i>=start_index. """ - d = abs(spike_time - spike_train[start_index]) - start_index += 1 + d = abs(spike_time - t_start) + if start_index < 0: + start_index = 0 while start_index < len(spike_train): d_temp = abs(spike_time - spike_train[start_index]) if d_temp > d: - break + return d else: d = d_temp start_index += 1 - return d + # finally, check the distance to end time + d_temp = abs(t_end - spike_time) + if d_temp > d: + return d + else: + return d_temp ############################################################ # spike_distance_python ############################################################ -def spike_distance_python(spikes1, spikes2): +def spike_distance_python(spikes1, spikes2, t_start, t_end): """ Computes the instantaneous spike-distance S_spike (t) of the two given spike trains. The spike trains are expected to have auxiliary spikes at the beginning and end of the interval. Use the function add_auxiliary_spikes to add those spikes to the spike train. Args: - spikes1, spikes2: ordered arrays of spike times with auxiliary spikes. + - t_start, t_end: edges of the spike train Returns: - PieceWiseLinFunc describing the spike-distance. """ - # check for auxiliary spikes - first and last spikes should be identical - assert spikes1[0] == spikes2[0], \ - "Given spike trains seems not to have auxiliary spikes!" - assert spikes1[-1] == spikes2[-1], \ - "Given spike trains seems not to have auxiliary spikes!" + # shorter variables t1 = spikes1 t2 = spikes2 - spike_events = np.empty(len(t1) + len(t2) - 2) - spike_events[0] = t1[0] - y_starts = np.empty(len(spike_events) - 1) - y_ends = np.empty(len(spike_events) - 1) - - index1 = 0 - index2 = 0 + N1 = len(t1) + N2 = len(t2) + + spike_events = np.empty(N1+N2+2) + + y_starts = np.empty(len(spike_events)-1) + y_ends = np.empty(len(spike_events)-1) + + spike_events[0] = t_start + t_p1 = t_start + t_p2 = t_start + if t1[0] > t_start: + t_f1 = t1[0] + dt_f1 = get_min_dist(t_f1, t2, 0, t_start, t_end) + dt_p1 = dt_f1 + isi1 = max(t_f1-t_start, t1[1]-t1[0]) + s1 = dt_p1*(t_f1-t_start)/isi1 + index1 = -1 + else: + dt_p1 = 0.0 + t_f1 = t1[1] + dt_f1 = get_min_dist(t_f1, t2, 0, t_start, t_end) + isi1 = t1[1]-t1[0] + s1 = dt_p1 + index1 = 0 + if t2[0] > t_start: + # dt_p1 = t2[0]-t_start + t_f2 = t2[0] + dt_f2 = get_min_dist(t_f2, t1, 0, t_start, t_end) + dt_p2 = dt_f2 + isi2 = max(t_f2-t_start, t2[1]-t2[0]) + s2 = dt_p2*(t_f2-t_start)/isi2 + index2 = -1 + else: + dt_p2 = 0.0 + t_f2 = t2[1] + dt_f2 = get_min_dist(t_f2, t1, 0, t_start, t_end) + isi2 = t2[1]-t2[0] + s2 = dt_p2 + index2 = 0 + + y_starts[0] = (s1*isi2 + s2*isi1) / (0.5*(isi1+isi2)**2) index = 1 - dt_p1 = 0.0 - dt_f1 = get_min_dist(t1[1], t2, 0) - dt_p2 = 0.0 - dt_f2 = get_min_dist(t2[1], t1, 0) - isi1 = max(t1[1]-t1[0], t1[2]-t1[1]) - isi2 = max(t2[1]-t2[0], t2[2]-t2[1]) - s1 = dt_f1*(t1[1]-t1[0])/isi1 - s2 = dt_f2*(t2[1]-t2[0])/isi2 - y_starts[0] = (s1*isi2 + s2*isi1) / ((isi1+isi2)**2/2) - while True: + + while index1+index2 < N1+N2-2: # print(index, index1, index2) - if t1[index1+1] < t2[index2+1]: + if (index1 < N1-1) and (t_f1 < t_f2 or index2 == N2-1): index1 += 1 - # break condition relies on existence of spikes at T_end - if index1+1 >= len(t1): - break - spike_events[index] = t1[index1] # first calculate the previous interval end value - dt_p1 = dt_f1 # the previous time was the following time before - s1 = dt_p1 - s2 = (dt_p2*(t2[index2+1]-t1[index1]) + - dt_f2*(t1[index1]-t2[index2])) / isi2 - y_ends[index-1] = (s1*isi2 + s2*isi1) / ((isi1+isi2)**2/2) + s1 = dt_f1*(t_f1-t_p1) / isi1 + # the previous time now was the following time before: + dt_p1 = dt_f1 + t_p1 = t_f1 # t_p1 contains the current time point + # get the next time + if index1 < N1-1: + t_f1 = t1[index1+1] + else: + t_f1 = t_end + spike_events[index] = t_p1 + s2 = (dt_p2*(t_f2-t_p1) + dt_f2*(t_p1-t_p2)) / isi2 + y_ends[index-1] = (s1*isi2 + s2*isi1) / (0.5*(isi1+isi2)**2) # now the next interval start value - dt_f1 = get_min_dist(t1[index1+1], t2, index2) - isi1 = t1[index1+1]-t1[index1] + if index1 < N1-1: + dt_f1 = get_min_dist(t_f1, t2, index2, t_start, t_end) + isi1 = t_f1-t_p1 + s1 = dt_p1 + else: + dt_f1 = dt_p1 + isi1 = max(t_end-t1[N1-1], t1[N1-1]-t1[N1-2]) + # s1 needs adjustment due to change of isi1 + s1 = dt_p1*(t_end-t1[N1-1])/isi1 # s2 is the same as above, thus we can compute y2 immediately - y_starts[index] = (s1*isi2 + s2*isi1) / ((isi1+isi2)**2/2) - elif t1[index1+1] > t2[index2+1]: + y_starts[index] = (s1*isi2 + s2*isi1) / (0.5*(isi1+isi2)**2) + elif (index2 < N2-1) and (t_f1 > t_f2 or index1 == N1-1): index2 += 1 - if index2+1 >= len(t2): - break - spike_events[index] = t2[index2] # first calculate the previous interval end value - dt_p2 = dt_f2 # the previous time was the following time before - s1 = (dt_p1*(t1[index1+1]-t2[index2]) + - dt_f1*(t2[index2]-t1[index1])) / isi1 - s2 = dt_p2 - y_ends[index-1] = (s1*isi2 + s2*isi1) / ((isi1+isi2)**2/2) + s2 = dt_f2*(t_f2-t_p2) / isi2 + # the previous time now was the following time before: + dt_p2 = dt_f2 + t_p2 = t_f2 # t_p1 contains the current time point + # get the next time + if index2 < N2-1: + t_f2 = t2[index2+1] + else: + t_f2 = t_end + spike_events[index] = t_p2 + s1 = (dt_p1*(t_f1-t_p2) + dt_f1*(t_p2-t_p1)) / isi1 + y_ends[index-1] = (s1*isi2 + s2*isi1) / (0.5*(isi1+isi2)**2) # now the next interval start value - dt_f2 = get_min_dist(t2[index2+1], t1, index1) - #s2 = dt_f2 - isi2 = t2[index2+1]-t2[index2] + if index2 < N2-1: + dt_f2 = get_min_dist(t_f2, t1, index1, t_start, t_end) + isi2 = t_f2-t_p2 + s2 = dt_p2 + else: + dt_f2 = dt_p2 + isi2 = max(t_end-t2[N2-1], t2[N2-1]-t2[N2-2]) + # s2 needs adjustment due to change of isi2 + s2 = dt_p2*(t_end-t2[N2-1])/isi2 # s2 is the same as above, thus we can compute y2 immediately - y_starts[index] = (s1*isi2 + s2*isi1) / ((isi1+isi2)**2/2) - else: # t1[index1+1] == t2[index2+1] - generate only one event + y_starts[index] = (s1*isi2 + s2*isi1) / (0.5*(isi1+isi2)**2) + else: # t_f1 == t_f2 - generate only one event index1 += 1 index2 += 1 - if (index1+1 >= len(t1)) or (index2+1 >= len(t2)): - break - assert dt_f2 == 0.0 - assert dt_f1 == 0.0 - spike_events[index] = t1[index1] - y_ends[index-1] = 0.0 - y_starts[index] = 0.0 + t_p1 = t_f1 + t_p2 = t_f2 dt_p1 = 0.0 dt_p2 = 0.0 - dt_f1 = get_min_dist(t1[index1+1], t2, index2) - dt_f2 = get_min_dist(t2[index2+1], t1, index1) - isi1 = t1[index1+1]-t1[index1] - isi2 = t2[index2+1]-t2[index2] + spike_events[index] = t_f1 + y_ends[index-1] = 0.0 + y_starts[index] = 0.0 + if index1 < N1-1: + t_f1 = t1[index1+1] + dt_f1 = get_min_dist(t_f1, t2, index2, t_start, t_end) + isi1 = t_f1 - t_p1 + else: + t_f1 = t_end + dt_f1 = dt_p1 + isi1 = max(t_end-t1[N1-1], t1[N1-1]-t1[N1-2]) + if index2 < N2-1: + t_f2 = t2[index2+1] + dt_f2 = get_min_dist(t_f2, t1, index1, t_start, t_end) + isi2 = t_f2 - t_p2 + else: + t_f2 = t_end + dt_f2 = dt_p2 + isi2 = max(t_end-t2[N2-1], t2[N2-1]-t2[N2-2]) index += 1 # the last event is the interval end - spike_events[index] = t1[-1] - # the ending value of the last interval - isi1 = max(t1[-1]-t1[-2], t1[-2]-t1[-3]) - isi2 = max(t2[-1]-t2[-2], t2[-2]-t2[-3]) - s1 = dt_p1*(t1[-1]-t1[-2])/isi1 - s2 = dt_p2*(t2[-1]-t2[-2])/isi2 - y_ends[index-1] = (s1*isi2 + s2*isi1) / ((isi1+isi2)**2/2) + if spike_events[index-1] == t_end: + index -= 1 + else: + spike_events[index] = t_end + # the ending value of the last interval + isi1 = max(t_end-t1[N1-1], t1[N1-1]-t1[N1-2]) + isi2 = max(t_end-t2[N2-1], t2[N2-1]-t2[N2-2]) + s1 = dt_f1*(t_end-t1[N1-1])/isi1 + s2 = dt_f2*(t_end-t2[N2-1])/isi2 + y_ends[index-1] = (s1*isi2 + s2*isi1) / (0.5*(isi1+isi2)**2) + # use only the data added above # could be less than original length due to equal spike times return spike_events[:index+1], y_starts[:index], y_ends[:index] @@ -245,47 +337,48 @@ def cumulative_sync_python(spikes1, spikes2): ############################################################ # coincidence_python ############################################################ -def coincidence_python(spikes1, spikes2, max_tau): +def coincidence_python(spikes1, spikes2, t_start, t_end, max_tau): def get_tau(spikes1, spikes2, i, j, max_tau): m = 1E100 # some huge number - if i < len(spikes1)-2: + if i < len(spikes1)-1 and i > -1: m = min(m, spikes1[i+1]-spikes1[i]) - if j < len(spikes2)-2: + if j < len(spikes2)-1 and j > -1: m = min(m, spikes2[j+1]-spikes2[j]) - if i > 1: + if i > 0: m = min(m, spikes1[i]-spikes1[i-1]) - if j > 1: + if j > 0: m = min(m, spikes2[j]-spikes2[j-1]) m *= 0.5 if max_tau > 0.0: m = min(m, max_tau) return m + N1 = len(spikes1) N2 = len(spikes2) - i = 0 - j = 0 + i = -1 + j = -1 n = 0 - st = np.zeros(N1 + N2 - 2) # spike times - c = np.zeros(N1 + N2 - 2) # coincidences - mp = np.ones(N1 + N2 - 2) # multiplicity - while n < N1 + N2 - 2: - if spikes1[i+1] < spikes2[j+1]: + st = np.zeros(N1 + N2 + 2) # spike times + c = 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, max_tau) st[n] = spikes1[i] - if j > 0 and spikes1[i]-spikes2[j] < tau: + if j > -1 and spikes1[i]-spikes2[j] < tau: # coincidence between the current spike and the previous spike # both get marked with 1 c[n] = 1 c[n-1] = 1 - elif spikes1[i+1] > spikes2[j+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, max_tau) st[n] = spikes2[j] - if i > 0 and spikes2[j]-spikes1[i] < tau: + if i > -1 and spikes2[j]-spikes1[i] < tau: # coincidence between the current spike and the previous spike # both get marked with 1 c[n] = 1 @@ -294,8 +387,6 @@ def coincidence_python(spikes1, spikes2, max_tau): # advance in both spike trains j += 1 i += 1 - if i == N1-1 or j == N2-1: - break n += 1 # add only one event, but with coincidence 2 and multiplicity 2 st[n] = spikes1[i] @@ -306,12 +397,12 @@ def coincidence_python(spikes1, spikes2, max_tau): c = c[:n+2] mp = mp[:n+2] - st[0] = spikes1[0] - st[-1] = spikes1[-1] + st[0] = t_start + st[len(st)-1] = t_end c[0] = c[1] - c[-1] = c[-2] + c[len(c)-1] = c[len(c)-2] mp[0] = mp[1] - mp[-1] = mp[-2] + mp[len(mp)-1] = mp[len(mp)-2] return st, c, mp |