diff options
Diffstat (limited to 'pyspike/distances.py')
| -rw-r--r-- | pyspike/distances.py | 99 |
1 files changed, 96 insertions, 3 deletions
diff --git a/pyspike/distances.py b/pyspike/distances.py index f4989c8..2ea80e7 100644 --- a/pyspike/distances.py +++ b/pyspike/distances.py @@ -7,11 +7,11 @@ Copyright 2014, Mario Mulansky <mario.mulansky@gmx.net> import numpy as np -from pyspike import PieceWiseConstFunc +from pyspike import PieceWiseConstFunc, PieceWiseLinFunc def isi_distance(spikes1, spikes2, T_end, T_start=0.0): - """ Computes the instantaneous isi-distance S_isi (t) of the two given spike - trains. + """ Computes the instantaneous isi-distance S_isi (t) of the two given + spike trains. Args: - spikes1, spikes2: ordered arrays of spike times. - T_end: end time of the observation interval. @@ -50,6 +50,7 @@ def isi_distance(spikes1, spikes2, T_end, T_start=0.0): # check which spike is next - from s1 or s2 if 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] @@ -63,3 +64,95 @@ def isi_distance(spikes1, spikes2, T_end, T_start=0.0): max(nu1[index1], nu2[index2]) index += 1 return PieceWiseConstFunc(spike_events, isi_values) + + +def get_min_dist(spike_time, spike_train, start_index=0): + """ Returns the minimal distance |spike_time - spike_train[i]| + with i>=start_index + """ + d = abs(spike_time - spike_train[start_index]) + start_index += 1 + while start_index < len(spike_train): + d_temp = abs(spike_time - spike_train[start_index]) + if d_temp > d: + break + else: + d = d_temp + start_index += 1 + return d + + +def spike_distance(spikes1, spikes2, T_end, T_start=0.0): + """ Computes the instantaneous spike-distance S_spike (t) of the two given + spike trains. + Args: + - spikes1, spikes2: ordered arrays of spike times. + - T_end: end time of the observation interval. + - T_start: begin of the observation interval (default=0.0). + Returns: + - PieceWiseLinFunc describing the spike-distance. + """ + # add spikes at the beginning and end of the interval + t1 = np.empty(len(spikes1)+2) + t1[0] = T_start + t1[-1] = T_end + t1[1:-1] = spikes1 + t2 = np.empty(len(spikes2)+2) + t2[0] = T_start + t2[-1] = T_end + t2[1:-1] = spikes2 + + spike_events = np.empty(len(t1)+len(t2)-2) + spike_events[0] = T_start + spike_events[-1] = T_end + y_starts = np.empty(len(spike_events)-1) + y_starts[0] = 0.0 + y_ends = np.empty(len(spike_events)-1) + + index1 = 0 + index2 = 0 + 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 = t1[1]-t1[0] + isi2 = t2[1]-t2[0] + while True: + print(index, index1, index2) + if t1[index1+1] < t2[index2+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 now 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) + # now the next interval start value + dt_f1 = get_min_dist(t1[index1+1], t2, index2) + s1 = dt_f1 + isi1 = t1[index1+1]-t1[index1] + # s2 is the same as above, thus we can compute y2 immediately + y_starts[index] = (s1*isi2 + s2*isi1) / ((isi1+isi2)**2/2) + else: + 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 now 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) + # 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] + # s2 is the same as above, thus we can compute y2 immediately + y_starts[index] = (s1*isi2 + s2*isi1) / ((isi1+isi2)**2/2) + index += 1 + + return PieceWiseLinFunc(spike_events, y_starts, y_ends) |