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Diffstat (limited to 'pyspike/generic.py')
| -rw-r--r-- | pyspike/generic.py | 150 |
1 files changed, 150 insertions, 0 deletions
diff --git a/pyspike/generic.py b/pyspike/generic.py new file mode 100644 index 0000000..5ad06f1 --- /dev/null +++ b/pyspike/generic.py @@ -0,0 +1,150 @@ +""" + +Generic functions to compute multi-variate profiles and distance matrices. + +Copyright 2015, Mario Mulansky <mario.mulansky@gmx.net> + +Distributed under the BSD License +""" + +from __future__ import division + +import numpy as np + + +############################################################ +# _generic_profile_multi +############################################################ +def _generic_profile_multi(spike_trains, pair_distance_func, indices=None): + """ Internal implementation detail, don't call this function directly, + use isi_profile_multi or spike_profile_multi instead. + + Computes the multi-variate distance for a set of spike-trains using the + pair_dist_func to compute pair-wise distances. That is it computes the + average distance of all pairs of spike-trains: + :math:`S(t) = 2/((N(N-1)) sum_{<i,j>} S_{i,j}`, + where the sum goes over all pairs <i,j>. + Args: + - spike_trains: list of spike trains + - pair_distance_func: function computing the distance of two spike trains + - indices: list of indices defining which spike trains to use, + if None all given spike trains are used (default=None) + Returns: + - The averaged multi-variate distance of all pairs + """ + + def divide_and_conquer(pairs1, pairs2): + """ recursive calls by splitting the two lists in half. + """ + L1 = len(pairs1) + if L1 > 1: + dist_prof1 = divide_and_conquer(pairs1[:L1//2], + pairs1[L1//2:]) + else: + dist_prof1 = pair_distance_func(spike_trains[pairs1[0][0]], + spike_trains[pairs1[0][1]]) + L2 = len(pairs2) + if L2 > 1: + dist_prof2 = divide_and_conquer(pairs2[:L2//2], + pairs2[L2//2:]) + else: + dist_prof2 = pair_distance_func(spike_trains[pairs2[0][0]], + spike_trains[pairs2[0][1]]) + dist_prof1.add(dist_prof2) + return dist_prof1 + + 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:]] + + L = len(pairs) + if L > 1: + # recursive iteration through the list of pairs to get average profile + avrg_dist = divide_and_conquer(pairs[:len(pairs)//2], + pairs[len(pairs)//2:]) + else: + avrg_dist = pair_distance_func(spike_trains[pairs[0][0]], + spike_trains[pairs[0][1]]) + + return avrg_dist, L + + +############################################################ +# _generic_distance_multi +############################################################ +def _generic_distance_multi(spike_trains, pair_distance_func, + indices=None, interval=None): + """ Internal implementation detail, don't call this function directly, + use isi_distance_multi or spike_distance_multi instead. + + Computes the multi-variate distance for a set of spike-trains using the + pair_dist_func to compute pair-wise distances. That is it computes the + average distance of all pairs of spike-trains: + :math:`S(t) = 2/((N(N-1)) sum_{<i,j>} D_{i,j}`, + where the sum goes over all pairs <i,j>. + Args: + - spike_trains: list of spike trains + - pair_distance_func: function computing the distance of two spike trains + - indices: list of indices defining which spike trains to use, + if None all given spike trains are used (default=None) + Returns: + - The averaged multi-variate distance of all pairs + """ + + 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:]] + + avrg_dist = 0.0 + for (i, j) in pairs: + avrg_dist += pair_distance_func(spike_trains[i], spike_trains[j], + interval) + + return avrg_dist/len(pairs) + + +############################################################ +# generic_distance_matrix +############################################################ +def _generic_distance_matrix(spike_trains, dist_function, + indices=None, interval=None): + """ Internal implementation detail. Don't use this function directly. + Instead use isi_distance_matrix or spike_distance_matrix. + Computes the time averaged distance of all pairs of spike-trains. + Args: + - spike_trains: list of spike trains + - indices: list of indices defining which spike-trains to use + if None all given spike-trains are used (default=None) + Return: + - a 2D array of size len(indices)*len(indices) containing the average + pair-wise distance + """ + 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 = [(i, j) for i in range(len(indices)) + for j in range(i+1, len(indices))] + + distance_matrix = np.zeros((len(indices), len(indices))) + for i, j in pairs: + d = dist_function(spike_trains[indices[i]], spike_trains[indices[j]], + interval) + distance_matrix[i, j] = d + distance_matrix[j, i] = d + return distance_matrix |