diff options
Diffstat (limited to 'pyspike/distances.py')
| -rw-r--r-- | pyspike/distances.py | 77 |
1 files changed, 39 insertions, 38 deletions
diff --git a/pyspike/distances.py b/pyspike/distances.py index db04c4e..b2eec92 100644 --- a/pyspike/distances.py +++ b/pyspike/distances.py @@ -17,7 +17,7 @@ from pyspike import PieceWiseConstFunc, PieceWiseLinFunc # isi_distance ############################################################ def isi_distance(spikes1, spikes2): - """ Computes the instantaneous isi-distance S_isi (t) of the two given + """ Computes the instantaneous isi-distance S_isi (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. @@ -27,9 +27,9 @@ def isi_distance(spikes1, spikes2): - PieceWiseConstFunc describing the isi-distance. """ # check for auxiliary spikes - first and last spikes should be identical - assert spikes1[0]==spikes2[0], \ + assert spikes1[0] == spikes2[0], \ "Given spike trains seems not to have auxiliary spikes!" - assert spikes1[-1]==spikes2[-1], \ + assert spikes1[-1] == spikes2[-1], \ "Given spike trains seems not to have auxiliary spikes!" # cython implementation @@ -53,9 +53,9 @@ def spike_distance(spikes1, spikes2): - PieceWiseLinFunc describing the spike-distance. """ # check for auxiliary spikes - first and last spikes should be identical - assert spikes1[0]==spikes2[0], \ + assert spikes1[0] == spikes2[0], \ "Given spike trains seems not to have auxiliary spikes!" - assert spikes1[-1]==spikes2[-1], \ + assert spikes1[-1] == spikes2[-1], \ "Given spike trains seems not to have auxiliary spikes!" # cython implementation @@ -74,33 +74,33 @@ def multi_distance(spike_trains, pair_distance_func, indices=None): 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 + 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}, + 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, + - 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: + 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." + "Invalid index list." # generate a list of possible index pairs - pairs = [(i,j) for i in indices for j in indices[i+1:]] + pairs = [(i, j) for i in indices for j in indices[i+1:]] # start with first pair - (i,j) = pairs[0] + (i, j) = pairs[0] average_dist = pair_distance_func(spike_trains[i], spike_trains[j]) - for (i,j) in pairs[1:]: + 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 + average_dist.add(current_dist) # add to the average + average_dist.mul_scalar(1.0/len(pairs)) # normalize return average_dist @@ -113,45 +113,46 @@ def multi_distance_par(spike_trains, pair_distance_func, indices=None): """ num_threads = 2 - lock = threading.Lock() + def run(spike_trains, index_pairs, average_dist): - (i,j) = index_pairs[0] + (i, j) = index_pairs[0] # print(i,j) this_avrg = pair_distance_func(spike_trains[i], spike_trains[j]) - for (i,j) in index_pairs[1:]: + for (i, j) in index_pairs[1:]: # print(i,j) current_dist = pair_distance_func(spike_trains[i], spike_trains[j]) this_avrg.add(current_dist) with lock: - average_dist.add(this_avrg) + average_dist.add(this_avrg) - if indices==None: + 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." + "Invalid index list." # generate a list of possible index pairs - pairs = [(i,j) for i in indices for j in indices[i+1:]] + pairs = [(i, j) for i in indices for j in indices[i+1:]] num_pairs = len(pairs) # start with first pair - (i,j) = pairs[0] + (i, j) = pairs[0] average_dist = pair_distance_func(spike_trains[i], spike_trains[j]) # remove the one we already computed pairs = pairs[1:] # distribute the rest into num_threads pieces - clustered_pairs = [ pairs[i::num_threads] for i in xrange(num_threads) ] + clustered_pairs = [pairs[n::num_threads] for n in xrange(num_threads)] threads = [] for pairs in clustered_pairs: - t = threading.Thread(target=run, args=(spike_trains, pairs, average_dist)) + t = threading.Thread(target=run, args=(spike_trains, pairs, + average_dist)) threads.append(t) t.start() for t in threads: t.join() - average_dist.mul_scalar(1.0/num_pairs) # normalize + average_dist.mul_scalar(1.0/num_pairs) # normalize return average_dist @@ -161,11 +162,11 @@ def multi_distance_par(spike_trains, pair_distance_func, indices=None): 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}, + 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, + - 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 @@ -177,13 +178,13 @@ def isi_distance_multi(spike_trains, indices=None): # spike_distance_multi ############################################################ def spike_distance_multi(spike_trains, indices=None): - """ computes the multi-variate spike-distance for a set of spike-trains. + """ 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}, + 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, + - 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 @@ -198,21 +199,21 @@ def isi_distance_matrix(spike_trains, indices=None): - 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 + - a 2D array of size len(indices)*len(indices) containing the average pair-wise isi-distance """ - if indices==None: + 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." + "Invalid index list." # generate a list of possible index pairs - pairs = [(i,j) for i in indices for j in indices[i+1:]] + pairs = [(i, j) for i in indices for j in indices[i+1:]] distance_matrix = np.zeros((len(indices), len(indices))) - for i,j in pairs: + for i, j in pairs: d = isi_distance(spike_trains[i], spike_trains[j]).abs_avrg() - distance_matrix[i,j] = d - distance_matrix[j,i] = d + distance_matrix[i, j] = d + distance_matrix[j, i] = d return distance_matrix |