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Diffstat (limited to 'pyspike/cython/cython_distance.pyx')
| -rw-r--r-- | pyspike/cython/cython_distance.pyx | 312 |
1 files changed, 312 insertions, 0 deletions
diff --git a/pyspike/cython/cython_distance.pyx b/pyspike/cython/cython_distance.pyx new file mode 100644 index 0000000..489aab9 --- /dev/null +++ b/pyspike/cython/cython_distance.pyx @@ -0,0 +1,312 @@ +#cython: boundscheck=False +#cython: wraparound=False +#cython: cdivision=True + +""" +cython_distances.pyx + +cython implementation of the isi- and spike-distance + +Note: using cython memoryviews (e.g. double[:]) instead of ndarray objects +improves the performance of spike_distance by a factor of 10! + +Copyright 2014, Mario Mulansky <mario.mulansky@gmx.net> + +Distributed under the BSD License + +""" + +""" +To test whether things can be optimized: remove all yellow stuff +in the html output:: + + cython -a cython_distance.pyx + +which gives:: + + cython_distance.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_distance_cython +############################################################ +def isi_distance_cython(double[:] s1, + double[:] s2): + + 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 + + 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) + + 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]: + 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]: + index2 += 1 + if index2 >= N2: + break + spike_events[index] = s2[index2] + nu2 = s2[index2+1]-s2[index2] + 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] + # 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] + # 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=0) 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 + while start_index < N: + d_temp = fabs(spike_time - spike_train[start_index]) + if d_temp > d: + break + else: + d = d_temp + start_index += 1 + return d + + +############################################################ +# 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 <S> ~ 0.5 for Poisson spikes + # return 0.5*(isi1*isi1+isi2*isi2) + + +############################################################ +# spike_distance_cython +############################################################ +def spike_distance_cython(double[:] t1, + double[:] t2): + + 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 + + N1 = len(t1) + N2 = len(t2) + + spike_events = np.empty(N1+N2-2) + spike_events[0] = t1[0] + 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 + y_starts[0] = (s1*isi2 + s2*isi1) / isi_avrg_cython(isi1, isi2) + 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 >= 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) + # now the next interval start value + dt_f1 = get_min_dist_cython(t1[index1+1], t2, N2, index2) + 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)/isi_avrg_cython(isi1, isi2) + elif t1[index1+1] > t2[index2+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) + # 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] + # 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 + 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 + 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] + 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) + # 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] + + + +############################################################ +# coincidence_python +############################################################ +cdef inline double get_tau(double[:] spikes1, double[:] spikes2, int i, int j): + cdef double m = 1E100 # some huge number + cdef int N1 = len(spikes1)-2 + cdef int N2 = len(spikes2)-2 + if i < N1: + m = fmin(m, spikes1[i+1]-spikes1[i]) + if j < N2: + m = fmin(m, spikes2[j+1]-spikes2[j]) + if i > 1: + m = fmin(m, spikes1[i]-spikes1[i-1]) + if j > 1: + m = fmin(m, spikes2[j]-spikes2[j-1]) + return 0.5*m + + +############################################################ +# coincidence_cython +############################################################ +def coincidence_cython(double[:] spikes1, double[:] spikes2): + + cdef int N1 = len(spikes1) + cdef int N2 = len(spikes2) + cdef int i = 0 + cdef int j = 0 + 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 tau + while n < N1 + N2 - 2: + if spikes1[i+1] < spikes2[j+1]: + i += 1 + n += 1 + tau = get_tau(spikes1, spikes2, i, j) + st[n] = spikes1[i] + if j > 0 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]: + j += 1 + n += 1 + tau = get_tau(spikes1, spikes2, i, j) + st[n] = spikes2[j] + if i > 0 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 + 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] + c[n] = 2 + mp[n] = 2 + + st = st[:n+2] + c = c[:n+2] + mp = mp[:n+2] + + st[0] = spikes1[0] + st[len(st)-1] = spikes1[len(spikes1)-1] + c[0] = c[1] + c[len(c)-1] = c[len(c)-2] + mp[0] = mp[1] + mp[len(mp)-1] = mp[len(mp)-2] + + return st, c, mp |