diff options
Diffstat (limited to 'pyspike/distances.py')
| -rw-r--r-- | pyspike/distances.py | 190 |
1 files changed, 112 insertions, 78 deletions
diff --git a/pyspike/distances.py b/pyspike/distances.py index 76dcd83..7a85471 100644 --- a/pyspike/distances.py +++ b/pyspike/distances.py @@ -63,6 +63,116 @@ def isi_distance(spikes1, spikes2): ############################################################ +# spike_distance +############################################################ +def spike_distance(spikes1, spikes2): + """ Computes the instantaneous spike-distance S_spike (t) of the two given + spike trains. The spike trains are expected to have auxiliary spikes at the + beginning and end of the interval. Use the function add_auxiliary_spikes to + add those spikes to the spike train. + Args: + - spikes1, spikes2: ordered arrays of spike times with auxiliary spikes. + Returns: + - PieceWiseLinFunc describing the spike-distance. + """ + # check for auxiliary spikes - first and last spikes should be identical + assert spikes1[0]==spikes2[0], \ + "Given spike trains seems not to have auxiliary spikes!" + assert spikes1[-1]==spikes2[-1], \ + "Given spike trains seems not to have auxiliary spikes!" + + # compile and load cython implementation + import pyximport + pyximport.install(setup_args={'include_dirs': [np.get_include()]}) + from cython_distance import spike_distance_cython + + times, y_starts, y_ends = spike_distance_cython(spikes1, spikes2) + + return PieceWiseLinFunc(times, y_starts, y_ends) + + +############################################################ +# multi_distance +############################################################ +def multi_distance(spike_trains, pair_distance_func, indices=None): + """ Internal implementation detail, use isi_distance_multi or + spike_distance_multi. + + 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: + 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 + """ + if indices==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 indices for j in indices[i+1:]] + # start with first pair + (i,j) = pairs[0] + average_dist = pair_distance_func(spike_trains[i], spike_trains[j]) + for (i,j) in pairs[1:]: + current_dist = pair_distance_func(spike_trains[i], spike_trains[j]) + average_dist.add(current_dist) # add to the average + average_dist.mul_scalar(1.0/len(pairs)) # normalize + return average_dist + + +############################################################ +# isi_distance_multi +############################################################ +def isi_distance_multi(spike_trains, indices=None): + """ computes the multi-variate isi-distance for a set of spike-trains. That + is the average isi-distance of all pairs of spike-trains: + 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 + - indices: list of indices defining which spike trains to use, + if None all given spike trains are used (default=None) + Returns: + - A PieceWiseConstFunc representing the averaged isi distance S + """ + return multi_distance(spike_trains, isi_distance, indices) + + +############################################################ +# spike_distance_multi +############################################################ +def spike_distance_multi(spike_trains, indices=None): + """ computes the multi-variate spike-distance for a set of spike-trains. + That is the average spike-distance of all pairs of spike-trains: + 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 + - indices: list of indices defining which spike trains to use, + if None all given spike trains are used (default=None) + Returns: + - A PieceWiseLinFunc representing the averaged spike distance S + """ + return multi_distance(spike_trains, spike_distance, indices) + + +############################################################ +############################################################ +# VANILLA PYTHON IMPLEMENTATIONS OF ISI AND SPIKE DISTANCE +############################################################ +############################################################ + + +############################################################ # isi_distance_python ############################################################ def isi_distance_python(s1, s2): @@ -134,9 +244,9 @@ def get_min_dist(spike_time, spike_train, start_index=0): ############################################################ -# spike_distance +# spike_distance_python ############################################################ -def spike_distance(spikes1, spikes2): +def spike_distance_python(spikes1, spikes2): """ Computes the instantaneous spike-distance S_spike (t) of the two given spike trains. The spike trains are expected to have auxiliary spikes at the beginning and end of the interval. Use the function add_auxiliary_spikes to @@ -235,79 +345,3 @@ def spike_distance(spikes1, spikes2): # could be less than original length due to equal spike times return PieceWiseLinFunc(spike_events[:index+1], y_starts[:index], y_ends[:index]) - - - - -############################################################ -# multi_distance -############################################################ -def multi_distance(spike_trains, pair_distance_func, indices=None): - """ Internal implementation detail, use isi_distance_multi or - spike_distance_multi. - - 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: - 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 - """ - if indices==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 indices for j in indices[i+1:]] - # start with first pair - (i,j) = pairs[0] - average_dist = pair_distance_func(spike_trains[i], spike_trains[j]) - for (i,j) in pairs[1:]: - current_dist = pair_distance_func(spike_trains[i], spike_trains[j]) - average_dist.add(current_dist) # add to the average - average_dist.mul_scalar(1.0/len(pairs)) # normalize - return average_dist - - -############################################################ -# isi_distance_multi -############################################################ -def isi_distance_multi(spike_trains, indices=None): - """ computes the multi-variate isi-distance for a set of spike-trains. That - is the average isi-distance of all pairs of spike-trains: - 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 - - indices: list of indices defining which spike trains to use, - if None all given spike trains are used (default=None) - Returns: - - A PieceWiseConstFunc representing the averaged isi distance S - """ - return multi_distance(spike_trains, isi_distance, indices) - - -############################################################ -# spike_distance_multi -############################################################ -def spike_distance_multi(spike_trains, indices=None): - """ computes the multi-variate spike-distance for a set of spike-trains. - That is the average spike-distance of all pairs of spike-trains: - 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 - - indices: list of indices defining which spike trains to use, - if None all given spike trains are used (default=None) - Returns: - - A PieceWiseLinFunc representing the averaged spike distance S - """ - return multi_distance(spike_trains, spike_distance, indices) |