diff options
Diffstat (limited to 'pyspike')
-rw-r--r-- | pyspike/__init__.py | 9 | ||||
-rw-r--r-- | pyspike/cython/cython_directionality.pyx | 223 | ||||
-rw-r--r-- | pyspike/cython/directionality_python_backend.py | 89 | ||||
-rw-r--r-- | pyspike/spike_directionality.py | 244 |
4 files changed, 563 insertions, 2 deletions
diff --git a/pyspike/__init__.py b/pyspike/__init__.py index 08253fb..7fa5265 100644 --- a/pyspike/__init__.py +++ b/pyspike/__init__.py @@ -7,8 +7,8 @@ Distributed under the BSD License from __future__ import absolute_import __all__ = ["isi_distance", "spike_distance", "spike_sync", "psth", - "spikes", "SpikeTrain", "PieceWiseConstFunc", "PieceWiseLinFunc", - "DiscreteFunc", "directionality"] + "spikes", "spike_directionality", "SpikeTrain", + "PieceWiseConstFunc", "PieceWiseLinFunc", "DiscreteFunc"] from .PieceWiseConstFunc import PieceWiseConstFunc from .PieceWiseLinFunc import PieceWiseLinFunc @@ -27,6 +27,11 @@ from .spikes import load_spike_trains_from_txt, save_spike_trains_to_txt, \ spike_train_from_string, import_spike_trains_from_time_series, \ merge_spike_trains, generate_poisson_spikes +from spike_directionality import spike_directionality, \ + spike_directionality_matrix, spike_train_order_profile, \ + optimal_spike_train_order_from_matrix, optimal_spike_train_order, \ + permutate_matrix + # define the __version__ following # http://stackoverflow.com/questions/17583443 from pkg_resources import get_distribution, DistributionNotFound diff --git a/pyspike/cython/cython_directionality.pyx b/pyspike/cython/cython_directionality.pyx new file mode 100644 index 0000000..e1f63c4 --- /dev/null +++ b/pyspike/cython/cython_directionality.pyx @@ -0,0 +1,223 @@ +#cython: boundscheck=False +#cython: wraparound=False +#cython: cdivision=True + +""" +cython_directionality.pyx + +cython implementation of the spike delay asymmetry measures + +Copyright 2015, 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_directionality.pyx + +which gives:: + + cython_directionality.html + +""" + +import numpy as np +cimport numpy as np + +from libc.math cimport fabs +from libc.math cimport fmax +from libc.math cimport fmin + +# from pyspike.cython.cython_distances cimport get_tau + +DTYPE = np.float +ctypedef np.float_t DTYPE_t + + +############################################################ +# 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 length 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 + + +############################################################ +# spike_train_order_profile_cython +############################################################ +def spike_train_order_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[:] a = np.zeros(N1 + N2 + 2) # asymmetry values + 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 + # spike from spike train 1 after spike train 2 + # both get marked with -1 + a[n] = -1 + a[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 + # spike from spike train 1 before spike train 2 + # both get marked with 1 + a[n] = 1 + a[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 with zero asymmetry value and multiplicity 2 + st[n] = spikes1[i] + a[n] = 0 + mp[n] = 2 + + st = st[:n+2] + a = a[:n+2] + mp = mp[:n+2] + + st[0] = t_start + st[len(st)-1] = t_end + if N1 + N2 > 0: + a[0] = a[1] + a[len(a)-1] = a[len(a)-2] + mp[0] = mp[1] + mp[len(mp)-1] = mp[len(mp)-2] + else: + a[0] = 1 + a[1] = 1 + + return st, a, mp + + + +############################################################ +# spike_order_values_cython +############################################################ +def spike_order_values_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 double[:] a1 = np.zeros(N1) # asymmetry values + cdef double[:] a2 = np.zeros(N2) # asymmetry values + 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 + tau = get_tau(spikes1, spikes2, i, j, interval, max_tau) + if j > -1 and spikes1[i]-spikes2[j] < tau: + # coincidence between the current spike and the previous spike + # spike from spike train 1 after spike train 2 + # leading spike gets +1, following spike -1 + a1[i] = -1 + a2[j] = +1 + elif (j < N2-1) and (i == N1-1 or spikes1[i+1] > spikes2[j+1]): + j += 1 + tau = get_tau(spikes1, spikes2, i, j, interval, max_tau) + if i > -1 and spikes2[j]-spikes1[i] < tau: + # coincidence between the current spike and the previous spike + # spike from spike train 1 before spike train 2 + # leading spike gets +1, following spike -1 + a1[i] = +1 + a2[j] = -1 + else: # spikes1[i+1] = spikes2[j+1] + # advance in both spike trains + j += 1 + i += 1 + # equal spike times: zero asymmetry value + a1[i] = 0 + a2[j] = 0 + + return a1, a2 + + +############################################################ +# spike_train_order_cython +############################################################ +def spike_train_order_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 asym = 0 + cdef int mp = 0 + 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 + mp += 1 + tau = get_tau(spikes1, spikes2, i, j, interval, max_tau) + if j > -1 and spikes1[i]-spikes2[j] < tau: + # coincidence between the current spike and the previous spike + # spike in spike train 2 appeared before spike in spike train 1 + # mark with -1 + asym -= 2 + elif (j < N2-1) and (i == N1-1 or spikes1[i+1] > spikes2[j+1]): + j += 1 + mp += 1 + tau = get_tau(spikes1, spikes2, i, j, interval, max_tau) + if i > -1 and spikes2[j]-spikes1[i] < tau: + # coincidence between the current spike and the previous spike + # spike in spike train 1 appeared before spike in spike train 2 + # mark with +1 + asym += 2 + else: # spikes1[i+1] = spikes2[j+1] + # advance in both spike trains + j += 1 + i += 1 + # add only one event with multiplicity 2, but no asymmetry counting + mp += 2 + + if asym == 0 and mp == 0: + # empty spike trains -> spike sync = 1 by definition + asym = 1 + mp = 1 + + return asym, mp diff --git a/pyspike/cython/directionality_python_backend.py b/pyspike/cython/directionality_python_backend.py new file mode 100644 index 0000000..e14238f --- /dev/null +++ b/pyspike/cython/directionality_python_backend.py @@ -0,0 +1,89 @@ +""" directionality_python_backend.py + +Collection of python functions that can be used instead of the cython +implementation. + +Copyright 2015, Mario Mulansky <mario.mulansky@gmx.net> + +Distributed under the BSD License + +""" + +import numpy as np + + +############################################################ +# spike_train_order_python +############################################################ +def spike_train_order_python(spikes1, spikes2, t_start, t_end, max_tau): + + def get_tau(spikes1, spikes2, i, j, max_tau): + m = t_end - t_start # use interval as initial tau + if i < len(spikes1)-1 and i > -1: + m = min(m, spikes1[i+1]-spikes1[i]) + if j < len(spikes2)-1 and j > -1: + m = min(m, spikes2[j+1]-spikes2[j]) + if i > 0: + m = min(m, spikes1[i]-spikes1[i-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 = -1 + j = -1 + n = 0 + st = np.zeros(N1 + N2 + 2) # spike times + a = 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 > -1 and spikes1[i]-spikes2[j] < tau: + # coincidence between the current spike and the previous spike + # both get marked with 1 + a[n] = -1 + a[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, 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 + a[n] = 1 + a[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 with zero asymmetry value and multiplicity 2 + st[n] = spikes1[i] + a[n] = 0 + mp[n] = 2 + + st = st[:n+2] + a = a[:n+2] + mp = mp[:n+2] + + st[0] = t_start + st[len(st)-1] = t_end + if N1 + N2 > 0: + a[0] = a[1] + a[len(a)-1] = a[len(a)-2] + mp[0] = mp[1] + mp[len(mp)-1] = mp[len(mp)-2] + else: + a[0] = 1 + a[1] = 1 + + return st, a, mp diff --git a/pyspike/spike_directionality.py b/pyspike/spike_directionality.py new file mode 100644 index 0000000..0e69cb5 --- /dev/null +++ b/pyspike/spike_directionality.py @@ -0,0 +1,244 @@ +# Module containing functions to compute the SPIKE directionality and the +# spike train order profile +# Copyright 2015, Mario Mulansky <mario.mulansky@gmx.net> +# Distributed under the BSD License + +import numpy as np +from math import exp +import pyspike +from pyspike import DiscreteFunc + + +############################################################ +# spike_directionality +############################################################ +def spike_directionality(spike_train1, spike_train2, normalize=True, + interval=None, max_tau=None): + """ Computes the overall spike directionality for two spike trains. + """ + if interval is None: + # distance over the whole interval is requested: use specific function + # for optimal performance + try: + from cython.cython_directionality import \ + spike_train_order_cython as spike_train_order_impl + if max_tau is None: + max_tau = 0.0 + c, mp = spike_train_order_impl(spike_train1.spikes, + spike_train2.spikes, + spike_train1.t_start, + spike_train1.t_end, + max_tau) + except ImportError: + # Cython backend not available: fall back to profile averaging + c, mp = _spike_directionality_profile(spike_train1, + spike_train2, + max_tau).integral(interval) + if normalize: + return 1.0*c/mp + else: + return c + else: + # some specific interval is provided: not yet implemented + raise NotImplementedError() + + +############################################################ +# spike_directionality_matrix +############################################################ +def spike_directionality_matrix(spike_trains, normalize=True, indices=None, + interval=None, max_tau=None): + """ Computes the spike directionaity matrix for the given spike trains. + """ + if indices is None: + indices = np.arange(len(spike_trains)) + indices = np.array(indices) + # check validity of indices + assert (indices < len(spike_trains)).all() and (indices >= 0).all(), \ + "Invalid index list." + # generate a list of possible index pairs + pairs = [(indices[i], j) for i in range(len(indices)) + for j in indices[i+1:]] + + distance_matrix = np.zeros((len(indices), len(indices))) + for i, j in pairs: + d = spike_directionality(spike_trains[i], spike_trains[j], normalize, + interval, max_tau=max_tau) + distance_matrix[i, j] = d + distance_matrix[j, i] = -d + return distance_matrix + + +############################################################ +# spike_train_order_profile +############################################################ +def spike_train_order_profile(spike_trains, indices=None, + interval=None, max_tau=None): + """ Computes the spike train symmetry value for each spike in each spike + train. + """ + if indices is None: + indices = np.arange(len(spike_trains)) + indices = np.array(indices) + # check validity of indices + assert (indices < len(spike_trains)).all() and (indices >= 0).all(), \ + "Invalid index list." + # list of arrays for reulting asymmetry values + asymmetry_list = [np.zeros_like(st.spikes) for st in spike_trains] + # generate a list of possible index pairs + pairs = [(indices[i], j) for i in range(len(indices)) + for j in indices[i+1:]] + + # cython implementation + try: + from cython.cython_directionality import \ + spike_order_values_cython as spike_order_values_impl + except ImportError: + raise NotImplementedError() +# if not(pyspike.disable_backend_warning): +# print("Warning: spike_distance_cython not found. Make sure that \ +# PySpike is installed by running\n 'python setup.py build_ext --inplace'!\n \ +# Falling back to slow python backend.") +# # use python backend +# from cython.python_backend import coincidence_python \ +# as coincidence_profile_impl + + if max_tau is None: + max_tau = 0.0 + + for i, j in pairs: + a1, a2 = spike_order_values_impl(spike_trains[i].spikes, + spike_trains[j].spikes, + spike_trains[i].t_start, + spike_trains[i].t_end, + max_tau) + asymmetry_list[i] += a1 + asymmetry_list[j] += a2 + for a in asymmetry_list: + a /= len(spike_trains)-1 + return asymmetry_list + + +############################################################ +# optimal_spike_train_order_from_matrix +############################################################ +def optimal_spike_train_order_from_matrix(D, full_output=False): + """ finds the best sorting via simulated annealing. + Returns the optimal permutation p and A value. + Internal function, don't call directly! Use optimal_asymmetry_order + instead. + """ + N = len(D) + A = np.sum(np.triu(D, 0)) + + p = np.arange(N) + + T = 2*np.max(D) # starting temperature + T_end = 1E-5 * T # final temperature + alpha = 0.9 # cooling factor + total_iter = 0 + while T > T_end: + iterations = 0 + succ_iter = 0 + while iterations < 100*N and succ_iter < 10*N: + # exchange two rows and cols + ind1 = np.random.randint(N-1) + delta_A = -2*D[p[ind1], p[ind1+1]] + if delta_A > 0.0 or exp(delta_A/T) > np.random.random(): + # swap indices + p[ind1], p[ind1+1] = p[ind1+1], p[ind1] + A += delta_A + succ_iter += 1 + iterations += 1 + total_iter += iterations + T *= alpha # cool down + if succ_iter == 0: + break + if full_output: + return p, A, total_iter + else: + return p, A + + +############################################################ +# optimal_spike_train_order +############################################################ +def optimal_spike_train_order(spike_trains, indices=None, interval=None, + max_tau=None, full_output=False): + """ finds the best sorting of the given spike trains via simulated + annealing. + Returns the optimal permutation p and A value. + """ + D = spike_directionality_matrix(spike_trains, normalize=False, + indices=indices, interval=interval, + max_tau=max_tau) + return optimal_spike_train_order_from_matrix(D, full_output) + + +############################################################ +# permutate_matrix +############################################################ +def permutate_matrix(D, p): + """ Applies the permutation p to the columns and rows of matrix D. + Return the new permutated matrix. + """ + N = len(D) + D_p = np.empty_like(D) + for n in xrange(N): + for m in xrange(N): + D_p[n, m] = D[p[n], p[m]] + return D_p + + +# internal helper functions + +############################################################ +# _spike_directionality_profile +############################################################ +def _spike_directionality_profile(spike_train1, spike_train2, + max_tau=None): + """ Computes the spike delay asymmetry profile A(t) of the two given + spike trains. Returns the profile as a DiscreteFunction object. + + :param spike_train1: First spike train. + :type spike_train1: :class:`pyspike.SpikeTrain` + :param spike_train2: Second spike train. + :type spike_train2: :class:`pyspike.SpikeTrain` + :param max_tau: Maximum coincidence window size. If 0 or `None`, the + coincidence window has no upper bound. + :returns: The spike-distance profile :math:`S_{sync}(t)`. + :rtype: :class:`pyspike.function.DiscreteFunction` + + """ + # check whether the spike trains are defined for the same interval + assert spike_train1.t_start == spike_train2.t_start, \ + "Given spike trains are not defined on the same interval!" + assert spike_train1.t_end == spike_train2.t_end, \ + "Given spike trains are not defined on the same interval!" + + # cython implementation + try: + from cython.cython_directionality import \ + spike_train_order_profile_cython as \ + spike_train_order_profile_impl + except ImportError: + # raise NotImplementedError() + if not(pyspike.disable_backend_warning): + print("Warning: spike_distance_cython not found. Make sure that \ +PySpike is installed by running\n 'python setup.py build_ext --inplace'!\n \ +Falling back to slow python backend.") + # use python backend + from cython.directionality_python_backend import \ + spike_train_order_python as spike_train_order_profile_impl + + if max_tau is None: + max_tau = 0.0 + + times, coincidences, multiplicity \ + = spike_train_order_profile_impl(spike_train1.spikes, + spike_train2.spikes, + spike_train1.t_start, + spike_train1.t_end, + max_tau) + + return DiscreteFunc(times, coincidences, multiplicity) |