#cython: boundscheck=False #cython: wraparound=False #cython: cdivision=True """ cython_profiles.pyx cython implementation of the isi-, spike- and spike-sync profiles Note: using cython memoryviews (e.g. double[:]) instead of ndarray objects improves the performance of spike_distance by a factor of 10! Copyright 2014-2015, Mario Mulansky Distributed under the BSD License """ """ To test whether things can be optimized: remove all yellow stuff in the html output:: cython -a cython_profiles.pyx which gives:: cython_profiles.html """ import numpy as np cimport numpy as np from libc.math cimport fabs from libc.math cimport fmax from libc.math cimport fmin DTYPE = np.float ctypedef np.float_t DTYPE_t ############################################################ # isi_profile_cython ############################################################ def isi_profile_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) 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]) if N1 > 1 else s1[0]-t_start index1 = -1 else: nu1 = s1[1]-s1[0] if N1 > 1 else t_end-s1[0] index1 = 0 if s2[0] > t_start: # edge correction nu2 = fmax(s2[0]-t_start, s2[1]-s2[0]) if N2 > 1 else s2[0]-t_start index2 = -1 else: nu2 = s2[1]-s2[0] if N2 > 1 else t_end-s2[0] index2 = 0 isi_values[0] = fabs(nu1-nu2)/fmax(nu1, nu2) index = 1 with nogil: # release the interpreter to allow multithreading 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 spike_events[index] = s1[index1] if index1 < N1-1: nu1 = s1[index1+1]-s1[index1] else: # edge correction nu1 = fmax(t_end-s1[index1], nu1) if N1 > 1 \ else t_end-s1[index1] elif (index2 < N2-1) and ((index1 == N1-1) or (s1[index1+1] > s2[index2+1])): index2 += 1 spike_events[index] = s2[index2] if index2 < N2-1: nu2 = s2[index2+1]-s2[index2] else: # edge correction nu2 = fmax(t_end-s2[index2], nu2) if N2 > 1 \ else t_end-s2[index2] else: # s1[index1+1] == s2[index2+1] index1 += 1 index2 += 1 spike_events[index] = s1[index1] if index1 < N1-1: nu1 = s1[index1+1]-s1[index1] else: # edge correction nu1 = fmax(t_end-s1[index1], nu1) if N1 > 1 \ else t_end-s1[index1] if index2 < N2-1: nu2 = s2[index2+1]-s2[index2] else: # edge correction nu2 = fmax(t_end-s2[index2], nu2) if N2 > 1 \ else t_end-s2[index2] # compute the corresponding isi-distance isi_values[index] = fabs(nu1 - nu2) / fmax(nu1, nu2) index += 1 # the last event is the interval end 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] ############################################################ # get_min_dist_cython ############################################################ cdef inline double get_min_dist_cython(double spike_time, double[:] spike_train, # use memory view to ensure inlining # np.ndarray[DTYPE_t,ndim=1] spike_train, int N, 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 # 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: return d else: d = d_temp start_index += 1 # finally, check the distance to end time d_temp = fabs(t_end - spike_time) if d_temp > d: return d else: return d_temp ############################################################ # isi_avrg_cython ############################################################ cdef inline double isi_avrg_cython(double isi1, double isi2) nogil: return 0.5*(isi1+isi2)*(isi1+isi2) # alternative definition to obtain ~ 0.5 for Poisson spikes # return 0.5*(isi1*isi1+isi2*isi2) ############################################################ # spike_profile_cython ############################################################ def spike_profile_cython(double[:] t1, double[:] t2, double t_start, double t_end): cdef double[:] spike_events cdef double[:] y_starts cdef double[:] y_ends cdef double[:] t_aux1 = np.empty(2) cdef double[:] t_aux2 = np.empty(2) cdef int N1, N2, index1, index2, index 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) # we can assume at least one spikes per spike train assert N1 > 0 assert N2 > 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 spike_events[0] = t_start # t_p1 = t_start # t_p2 = t_start # auxiliary spikes for edge correction - consistent with first/last ISI t_aux1[0] = fmin(t_start, 2*t1[0]-t1[1]) if N1 > 1 else t_start t_aux1[1] = fmax(t_end, 2*t1[N1-1]-t1[N1-2]) if N1 > 1 else t_end t_aux2[0] = fmin(t_start, 2*t2[0]-t2[1]) if N2 > 1 else t_start t_aux2[1] = fmax(t_end, 2*t2[N2-1]-t2[N2-2]) if N2 > 1 else t_end t_p1 = t_start if (t1[0] == t_start) else t_aux1[0] t_p2 = t_start if (t2[0] == t_start) else t_aux2[0] 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_aux2[0], t_aux2[1]) isi1 = fmax(t_f1-t_start, t1[1]-t1[0]) if N1 > 1 else t_f1-t_start dt_p1 = dt_f1 # s1 = dt_p1*(t_f1-t_start)/isi1 s1 = dt_p1 index1 = -1 else: t_f1 = t1[1] if N1 > 1 else t_end dt_f1 = get_min_dist_cython(t_f1, t2, N2, 0, t_aux2[0], t_aux2[1]) dt_p1 = get_min_dist_cython(t_p1, t2, N2, 0, t_aux2[0], t_aux2[1]) isi1 = t_f1-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_aux1[0], t_aux1[1]) dt_p2 = dt_f2 isi2 = fmax(t_f2-t_start, t2[1]-t2[0]) if N2 > 1 else t_f2-t_start # s2 = dt_p2*(t_f2-t_start)/isi2 s2 = dt_p2 index2 = -1 else: t_f2 = t2[1] if N2 > 1 else t_end dt_f2 = get_min_dist_cython(t_f2, t1, N1, 0, t_aux1[0], t_aux1[1]) dt_p2 = get_min_dist_cython(t_p2, t1, N1, 0, t_aux1[0], t_aux1[1]) isi2 = t_f2-t2[0] s2 = dt_p2 index2 = 0 y_starts[0] = (s1*isi2 + s2*isi1) / isi_avrg_cython(isi1, isi2) index = 1 while index1+index2 < N1+N2-2: # print(index, index1, index2) if (index1 < N1-1) and (t_f1 < t_f2 or index2 == N2-1): index1 += 1 # first calculate the previous interval end value 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_aux1[1] 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 if index1 < N1-1: dt_f1 = get_min_dist_cython(t_f1, t2, N2, index2, t_aux2[0], t_aux2[1]) 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]) if N1 > 1 \ else t_end-t1[N1-1] # s1 needs adjustment due to change of isi1 # s1 = dt_p1*(t_end-t1[N1-1])/isi1 # Eero's correction: no adjustment s1 = dt_p1 # 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 (index2 < N2-1) and (t_f1 > t_f2 or index1 == N1-1): index2 += 1 # first calculate the previous interval end value 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_aux2[1] 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 if index2 < N2-1: dt_f2 = get_min_dist_cython(t_f2, t1, N1, index1, t_aux1[0], t_aux1[1]) 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]) if N2 > 1 \ else t_end-t2[N2-1] # s2 needs adjustment due to change of isi2 # s2 = dt_p2*(t_end-t2[N2-1])/isi2 # Eero's correction: no adjustment s2 = dt_p2 # 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: # t_f1 == t_f2 - generate only one event index1 += 1 index2 += 1 t_p1 = t_f1 t_p2 = t_f2 dt_p1 = 0.0 dt_p2 = 0.0 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_aux2[0], t_aux2[1]) isi1 = t_f1 - t_p1 else: t_f1 = t_aux1[1] dt_f1 = dt_p1 isi1 = fmax(t_end-t1[N1-1], t1[N1-1]-t1[N1-2]) if N1 > 1 \ else t_end-t1[N1-1] if index2 < N2-1: t_f2 = t2[index2+1] dt_f2 = get_min_dist_cython(t_f2, t1, N1, index1, t_aux1[0], t_aux1[1]) isi2 = t_f2 - t_p2 else: t_f2 = t_aux2[1] dt_f2 = dt_p2 isi2 = fmax(t_end-t2[N2-1], t2[N2-1]-t2[N2-2]) if N2 > 1 \ else t_end-t2[N2-1] index += 1 # the last event is the interval end if spike_events[index-1] == t_end: index -= 1 else: spike_events[index] = t_end s1 = dt_f1 s2 = dt_f2 y_ends[index-1] = (s1*isi2 + s2*isi1) / isi_avrg_cython(isi1, isi2) # end nogil # 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] ############################################################ # get_tau ############################################################ cdef inline double get_tau(double[:] spikes1, double[:] spikes2, int i, int j, double interval, double max_tau): cdef double m = interval # use interval as initial tau 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 and j > -1: m = fmin(m, spikes2[j+1]-spikes2[j]) if i > 0: m = fmin(m, spikes1[i]-spikes1[i-1]) if j > 0: m = fmin(m, spikes2[j]-spikes2[j-1]) m *= 0.5 if max_tau > 0.0: m = fmin(m, max_tau) return m ############################################################ # coincidence_profile_cython ############################################################ def coincidence_profile_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 = -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 interval = t_end - t_start cdef double tau 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, interval, max_tau) 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 c[n] = 1 c[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, interval, max_tau) 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 c[n] = 1 c[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, but with coincidence 2 and multiplicity 2 st[n] = spikes1[i] c[n] = 2 mp[n] = 2 st = st[:n+2] c = c[:n+2] mp = mp[:n+2] st[0] = t_start st[len(st)-1] = t_end if N1 + N2 > 0: c[0] = c[1] c[len(c)-1] = c[len(c)-2] mp[0] = mp[1] mp[len(mp)-1] = mp[len(mp)-2] else: c[0] = 1 c[1] = 1 return st, c, mp