diff options
Diffstat (limited to 'pyspike/cython/cython_distance.pyx')
| -rw-r--r-- | pyspike/cython/cython_distance.pyx | 339 |
1 files changed, 223 insertions, 116 deletions
diff --git a/pyspike/cython/cython_distance.pyx b/pyspike/cython/cython_distance.pyx index 2834ca5..6ee0181 100644 --- a/pyspike/cython/cython_distance.pyx +++ b/pyspike/cython/cython_distance.pyx @@ -42,57 +42,88 @@ ctypedef np.float_t DTYPE_t ############################################################ # isi_distance_cython ############################################################ -def isi_distance_cython(double[:] s1, - double[:] s2): +def isi_distance_cython(double[:] s1, double[:] s2, + double t_start, double t_end): cdef double[:] spike_events cdef double[:] isi_values cdef int index1, index2, index cdef int N1, N2 cdef double nu1, nu2 - N1 = len(s1)-1 - N2 = len(s2)-1 + N1 = len(s1) + N2 = len(s2) + + spike_events = np.empty(N1+N2+2) + # the values have one entry less as they are defined at the intervals + isi_values = np.empty(N1+N2+1) + + # first x-value of the profile + spike_events[0] = t_start + + # first interspike interval - check if a spike exists at the start time + if s1[0] > t_start: + # edge correction + nu1 = fmax(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 = fmax(s2[0]-t_start, s2[1]-s2[0]) + index2 = -1 + else: + nu2 = s2[1]-s2[0] + index2 = 0 - nu1 = s1[1]-s1[0] - nu2 = s2[1]-s2[0] - spike_events = np.empty(N1+N2) - spike_events[0] = s1[0] - # the values have one entry less - the number of intervals between events - isi_values = np.empty(N1+N2-1) + isi_values[0] = fabs(nu1-nu2)/fmax(nu1, nu2) + index = 1 with nogil: # release the interpreter to allow multithreading - isi_values[0] = fabs(nu1-nu2)/fmax(nu1, nu2) - index1 = 0 - index2 = 0 - index = 1 - while True: - # check which spike is next - from s1 or s2 - if s1[index1+1] < s2[index2+1]: + while index1+index2 < N1+N2-2: + # check which spike is next, only if there are spikes left in 1 + # next spike in 1 is earlier, or there are no spikes left in 2 + 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 >= N1: - break spike_events[index] = s1[index1] - nu1 = s1[index1+1]-s1[index1] - elif s1[index1+1] > s2[index2+1]: + if index1 < N1-1: + nu1 = s1[index1+1]-s1[index1] + else: + # edge correction + nu1 = fmax(t_end-s1[index1], nu1) + elif (index2 < N2-1) and ((index1 == N1-1) or + (s1[index1+1] > s2[index2+1])): index2 += 1 - if index2 >= N2: - break spike_events[index] = s2[index2] - nu2 = s2[index2+1]-s2[index2] + if index2 < N2-1: + nu2 = s2[index2+1]-s2[index2] + else: + # edge correction + nu2 = fmax(t_end-s2[index2], nu2) else: # s1[index1+1] == s2[index2+1] index1 += 1 index2 += 1 - if (index1 >= N1) or (index2 >= N2): - break spike_events[index] = s1[index1] - nu1 = s1[index1+1]-s1[index1] - nu2 = s2[index2+1]-s2[index2] + if index1 < N1-1: + nu1 = s1[index1+1]-s1[index1] + else: + # edge correction + nu1 = fmax(t_end-s1[index1], nu1) + if index2 < N2-1: + nu2 = s2[index2+1]-s2[index2] + else: + # edge correction + nu2 = fmax(t_end-s2[index2], nu2) # compute the corresponding isi-distance isi_values[index] = fabs(nu1 - nu2) / fmax(nu1, nu2) index += 1 # the last event is the interval end - spike_events[index] = s1[N1] + if spike_events[index-1] == t_end: + index -= 1 + else: + spike_events[index] = t_end # end nogil return spike_events[:index+1], isi_values[:index] @@ -106,21 +137,30 @@ cdef inline double get_min_dist_cython(double spike_time, # use memory view to ensure inlining # np.ndarray[DTYPE_t,ndim=1] spike_train, int N, - int start_index=0) nogil: + int start_index, + double t_start, double t_end) nogil: """ Returns the minimal distance |spike_time - spike_train[i]| with i>=start_index. """ cdef double d, d_temp - d = fabs(spike_time - spike_train[start_index]) - start_index += 1 + # start with the distance to the start time + d = fabs(spike_time - t_start) + if start_index < 0: + start_index = 0 while start_index < N: d_temp = fabs(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 = fabs(t_end - spike_time) + if d_temp > d: + return d + else: + return d_temp ############################################################ @@ -135,96 +175,164 @@ cdef inline double isi_avrg_cython(double isi1, double isi2) nogil: ############################################################ # spike_distance_cython ############################################################ -def spike_distance_cython(double[:] t1, - double[:] t2): +def spike_distance_cython(double[:] t1, double[:] t2, + double t_start, double t_end): cdef double[:] spike_events cdef double[:] y_starts cdef double[:] y_ends cdef int N1, N2, index1, index2, index - cdef double dt_p1, dt_p2, dt_f1, dt_f2, isi1, isi2, s1, s2 + cdef double t_p1, t_f1, t_p2, t_f2, dt_p1, dt_p2, dt_f1, dt_f2 + cdef double isi1, isi2, s1, s2 N1 = len(t1) N2 = len(t2) - spike_events = np.empty(N1+N2-2) - spike_events[0] = t1[0] + spike_events = np.empty(N1+N2+2) + y_starts = np.empty(len(spike_events)-1) y_ends = np.empty(len(spike_events)-1) with nogil: # release the interpreter to allow multithreading - index1 = 0 - index2 = 0 - index = 1 - dt_p1 = 0.0 - dt_f1 = get_min_dist_cython(t1[1], t2, N2, 0) - dt_p2 = 0.0 - dt_f2 = get_min_dist_cython(t2[1], t1, N1, 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 + spike_events[0] = t_start + t_p1 = t_start + t_p2 = t_start + if t1[0] > t_start: + # dt_p1 = t2[0]-t_start + t_f1 = t1[0] + dt_f1 = get_min_dist_cython(t_f1, t2, N2, 0, t_start, t_end) + isi1 = fmax(t_f1-t_start, t1[1]-t1[0]) + dt_p1 = dt_f1 + s1 = dt_p1*(t_f1-t_start)/isi1 + index1 = -1 + else: + t_f1 = t1[1] + dt_f1 = get_min_dist_cython(t_f1, t2, N2, 0, t_start, t_end) + dt_p1 = 0.0 + 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_cython(t_f2, t1, N1, 0, t_start, t_end) + dt_p2 = dt_f2 + isi2 = fmax(t_f2-t_start, t2[1]-t2[0]) + s2 = dt_p2*(t_f2-t_start)/isi2 + index2 = -1 + else: + t_f2 = t2[1] + dt_f2 = get_min_dist_cython(t_f2, t1, N1, 0, t_start, t_end) + dt_p2 = 0.0 + isi2 = t2[1]-t2[0] + s2 = dt_p2 + index2 = 0 + y_starts[0] = (s1*isi2 + s2*isi1) / isi_avrg_cython(isi1, isi2) - while True: + index = 1 + + 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 >= N1: - 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)/isi_avrg_cython(isi1, isi2) + 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)/isi_avrg_cython(isi1, + isi2) # now the next interval start value - dt_f1 = get_min_dist_cython(t1[index1+1], t2, N2, index2) - isi1 = t1[index1+1]-t1[index1] + if index1 < N1-1: + dt_f1 = get_min_dist_cython(t_f1, t2, N2, index2, + t_start, t_end) + isi1 = t_f1-t_p1 + s1 = dt_p1 + else: + dt_f1 = dt_p1 + isi1 = fmax(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)/isi_avrg_cython(isi1, isi2) - elif t1[index1+1] > t2[index2+1]: + y_starts[index] = (s1*isi2 + s2*isi1)/isi_avrg_cython(isi1, + isi2) + elif (index2 < N2-1) and (t_f1 > t_f2 or index1 == N1-1): index2 += 1 - if index2+1 >= N2: - 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) / isi_avrg_cython(isi1, isi2) + 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_p2 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) / isi_avrg_cython(isi1, + isi2) # now the next interval start value - dt_f2 = get_min_dist_cython(t2[index2+1], t1, N1, index1) - #s2 = dt_f2 - isi2 = t2[index2+1]-t2[index2] + if index2 < N2-1: + dt_f2 = get_min_dist_cython(t_f2, t1, N1, index1, + t_start, t_end) + isi2 = t_f2-t_p2 + s2 = dt_p2 + else: + dt_f2 = dt_p2 + isi2 = fmax(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)/isi_avrg_cython(isi1, isi2) - else: # t1[index1+1] == t2[index2+1] - generate only one event + else: # t_f1 == t_f2 - generate only one event index1 += 1 index2 += 1 - if (index1+1 >= N1) or (index2+1 >= N2): - break - 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_cython(t1[index1+1], t2, N2, index2) - dt_f2 = get_min_dist_cython(t2[index2+1], t1, N1, 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_cython(t_f1, t2, N2, index2, + t_start, t_end) + isi1 = t_f1 - t_p1 + else: + t_f1 = t_end + dt_f1 = dt_p1 + isi1 = fmax(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_cython(t_f2, t1, N1, index1, + t_start, t_end) + isi2 = t_f2 - t_p2 + else: + t_f2 = t_end + dt_f2 = dt_p2 + isi2 = fmax(t_end-t2[N2-1], t2[N2-1]-t2[N2-2]) index += 1 # the last event is the interval end - spike_events[index] = t1[N1-1] - # the ending value of the last interval - isi1 = max(t1[N1-1]-t1[N1-2], t1[N1-2]-t1[N1-3]) - isi2 = max(t2[N2-1]-t2[N2-2], t2[N2-2]-t2[N2-3]) - s1 = dt_p1*(t1[N1-1]-t1[N1-2])/isi1 - s2 = dt_p2*(t2[N2-1]-t2[N2-2])/isi2 - y_ends[index-1] = (s1*isi2 + s2*isi1) / isi_avrg_cython(isi1, isi2) + 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) / isi_avrg_cython(isi1, isi2) # end nogil # use only the data added above @@ -237,17 +345,17 @@ def spike_distance_cython(double[:] t1, # coincidence_python ############################################################ cdef inline double get_tau(double[:] spikes1, double[:] spikes2, - int i, int j, max_tau): + int i, int j, double max_tau): cdef double m = 1E100 # some huge number - cdef int N1 = len(spikes1)-2 - cdef int N2 = len(spikes2)-2 - if i < N1: + cdef int N1 = spikes1.shape[0]-1 # len(spikes1)-1 + cdef int N2 = spikes2.shape[0]-1 # len(spikes2)-1 + if i < N1 and i > -1: m = fmin(m, spikes1[i+1]-spikes1[i]) - if j < N2: + if j < N2 and j > -1: m = fmin(m, spikes2[j+1]-spikes2[j]) - if i > 1: + if i > 0: m = fmin(m, spikes1[i]-spikes1[i-1]) - if j > 1: + if j > 0: m = fmin(m, spikes2[j]-spikes2[j-1]) m *= 0.5 if max_tau > 0.0: @@ -258,34 +366,35 @@ cdef inline double get_tau(double[:] spikes1, double[:] spikes2, ############################################################ # coincidence_cython ############################################################ -def coincidence_cython(double[:] spikes1, double[:] spikes2, double max_tau): +def coincidence_cython(double[:] spikes1, double[:] spikes2, + double t_start, double t_end, double max_tau): cdef int N1 = len(spikes1) cdef int N2 = len(spikes2) - cdef int i = 0 - cdef int j = 0 + cdef int i = -1 + cdef int j = -1 cdef int n = 0 - cdef double[:] st = np.zeros(N1 + N2 - 2) # spike times - cdef double[:] c = np.zeros(N1 + N2 - 2) # coincidences - cdef double[:] mp = np.ones(N1 + N2 - 2) # multiplicity + cdef double[:] st = np.zeros(N1 + N2 + 2) # spike times + cdef double[:] c = np.zeros(N1 + N2 + 2) # coincidences + cdef double[:] mp = np.ones(N1 + N2 + 2) # multiplicity cdef double tau - while n < N1 + N2 - 2: - if spikes1[i+1] < spikes2[j+1]: + 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 +403,6 @@ def coincidence_cython(double[:] spikes1, double[:] spikes2, double 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] @@ -306,8 +413,8 @@ def coincidence_cython(double[:] spikes1, double[:] spikes2, double max_tau): c = c[:n+2] mp = mp[:n+2] - st[0] = spikes1[0] - st[len(st)-1] = spikes1[len(spikes1)-1] + st[0] = t_start + st[len(st)-1] = t_end c[0] = c[1] c[len(c)-1] = c[len(c)-2] mp[0] = mp[1] |