new generic interface for spike_sync functions

Similar to the isi and spike distance functions, also the spike sync functions
now support the new generic interface.

pyspike/spike_sync.py

@@ -15,7 +15,40 @@
 ############################################################
 # spike_sync_profile
 ############################################################
-def spike_sync_profile(spike_train1, spike_train2, max_tau=None):
+def spike_sync_profile(*args, **kwargs):
+    """ Computes the spike-synchronization profile S_sync(t) of the given
+    spike trains. Returns the profile as a DiscreteFunction object. In the
+    bivariate case, he S_sync values are either 1 or 0, indicating the presence
+    or absence of a coincidence. For multi-variate cases, each spike in the set
+    of spike trains, the profile is defined as the number of coincidences
+    divided by the number of spike trains pairs involving the spike train of
+    containing this spike, which is the number of spike trains minus one (N-1).
+
+    Valid call structures::
+
+      spike_sync_profile(st1, st2)  # returns the bi-variate profile
+      spike_sync_profile(st1, st2, st3)  # multi-variate profile of 3 sts
+
+      sts = [st1, st2, st3, st4]  # list of spike trains
+      spike_sync_profile(sts)  # profile of the list of spike trains
+      spike_sync_profile(sts, indices=[0, 1])  # use only the spike trains
+                                               # given by the indices
+
+    :returns: The spike-sync profile :math:`S_{sync}(t)`.
+    :rtype: :class:`pyspike.function.DiscreteFunction`
+    """
+    if len(args) == 1:
+        return spike_sync_profile_multi(args[0], **kwargs)
+    elif len(args) == 2:
+        return spike_sync_profile_bi(args[0], args[1])
+    else:
+        return spike_sync_profile_multi(args)
+
+
+############################################################
+# spike_sync_profile_bi
+############################################################
+def spike_sync_profile_bi(spike_train1, spike_train2, max_tau=None):
     """ Computes the spike-synchronization profile S_sync(t) of the two given
     spike trains. Returns the profile as a DiscreteFunction object. The S_sync
     values are either 1 or 0, indicating the presence or absence of a
@@ -27,7 +60,7 @@ def spike_sync_profile(spike_train1, spike_train2, max_tau=None):
     :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)`.
+    :returns: The spike-sync profile :math:`S_{sync}(t)`.
     :rtype: :class:`pyspike.function.DiscreteFunction`
 
     """
@@ -61,6 +94,33 @@ def spike_sync_profile(spike_train1, spike_train2, max_tau=None):
     return DiscreteFunc(times, coincidences, multiplicity)
 
 
+############################################################
+# spike_sync_profile_multi
+############################################################
+def spike_sync_profile_multi(spike_trains, indices=None, max_tau=None):
+    """ Computes the multi-variate spike synchronization profile for a set of
+    spike trains. For each spike in the set of spike trains, the multi-variate
+    profile is defined as the number of coincidences divided by the number of
+    spike trains pairs involving the spike train of containing this spike,
+    which is the number of spike trains minus one (N-1).
+
+    :param spike_trains: list of :class:`pyspike.SpikeTrain`
+    :param indices: list of indices defining which spike trains to use,
+                    if None all given spike trains are used (default=None)
+    :type indices: list or None
+    :param max_tau: Maximum coincidence window size. If 0 or `None`, the
+                    coincidence window has no upper bound.
+    :returns: The multi-variate spike sync profile :math:`<S_{sync}>(t)`
+    :rtype: :class:`pyspike.function.DiscreteFunction`
+
+    """
+    prof_func = partial(spike_sync_profile_bi, max_tau=max_tau)
+    average_prof, M = _generic_profile_multi(spike_trains, prof_func,
+                                             indices)
+    # average_dist.mul_scalar(1.0/M)  # no normalization here!
+    return average_prof
+
+
 ############################################################
 # _spike_sync_values
 ############################################################
@@ -87,18 +147,51 @@ def _spike_sync_values(spike_train1, spike_train2, interval, max_tau):
             return c, mp
         except ImportError:
             # Cython backend not available: fall back to profile averaging
-            return spike_sync_profile(spike_train1, spike_train2,
-                                      max_tau).integral(interval)
+            return spike_sync_profile_bi(spike_train1, spike_train2,
+                                         max_tau).integral(interval)
     else:
         # some specific interval is provided: use profile
-        return spike_sync_profile(spike_train1, spike_train2,
-                                  max_tau).integral(interval)
+        return spike_sync_profile_bi(spike_train1, spike_train2,
+                                     max_tau).integral(interval)
 
 
 ############################################################
 # spike_sync
 ############################################################
-def spike_sync(spike_train1, spike_train2, interval=None, max_tau=None):
+def spike_sync(*args, **kwargs):
+    """ Computes the spike synchronization value SYNC of the given spike
+    trains. The spike synchronization value is the computed as the total number
+    of coincidences divided by the total number of spikes:
+
+    .. math:: SYNC = \sum_n C_n / N.
+
+
+    Valid call structures::
+
+      spike_sync(st1, st2)  # returns the bi-variate spike synchronization
+      spike_sync(st1, st2, st3)  # multi-variate result for 3 spike trains
+
+      spike_trains = [st1, st2, st3, st4]  # list of spike trains
+      spike_sync(spike_trains)  # spike-sync of the list of spike trains
+      spike_sync(spike_trains, indices=[0, 1])  # use only the spike trains
+                                                # given by the indices
+
+    :returns: The spike synchronization value.
+    :rtype: `double`
+    """
+
+    if len(args) == 1:
+        return spike_sync_multi(args[0], **kwargs)
+    elif len(args) == 2:
+        return spike_sync_bi(args[0], args[1], **kwargs)
+    else:
+        return spike_sync_multi(args, **kwargs)
+
+
+############################################################
+# spike_sync_bi
+############################################################
+def spike_sync_bi(spike_train1, spike_train2, interval=None, max_tau=None):
     """ Computes the spike synchronization value SYNC of the given spike
     trains. The spike synchronization value is the computed as the total number
     of coincidences divided by the total number of spikes:
@@ -122,33 +215,6 @@ def spike_sync(spike_train1, spike_train2, interval=None, max_tau=None):
     return 1.0*c/mp
 
 
-############################################################
-# spike_sync_profile_multi
-############################################################
-def spike_sync_profile_multi(spike_trains, indices=None, max_tau=None):
-    """ Computes the multi-variate spike synchronization profile for a set of
-    spike trains. For each spike in the set of spike trains, the multi-variate
-    profile is defined as the number of coincidences divided by the number of
-    spike trains pairs involving the spike train of containing this spike,
-    which is the number of spike trains minus one (N-1).
-
-    :param spike_trains: list of :class:`pyspike.SpikeTrain`
-    :param indices: list of indices defining which spike trains to use,
-                    if None all given spike trains are used (default=None)
-    :type indices: list or None
-    :param max_tau: Maximum coincidence window size. If 0 or `None`, the
-                    coincidence window has no upper bound.
-    :returns: The multi-variate spike sync profile :math:`<S_{sync}>(t)`
-    :rtype: :class:`pyspike.function.DiscreteFunction`
-
-    """
-    prof_func = partial(spike_sync_profile, max_tau=max_tau)
-    average_prof, M = _generic_profile_multi(spike_trains, prof_func,
-                                             indices)
-    # average_dist.mul_scalar(1.0/M)  # no normalization here!
-    return average_prof
-
-
 ############################################################
 # spike_sync_multi
 ############################################################
@@ -211,6 +277,6 @@ def spike_sync_matrix(spike_trains, indices=None, interval=None, max_tau=None):
     :rtype: np.array
 
     """
-    dist_func = partial(spike_sync, max_tau=max_tau)
+    dist_func = partial(spike_sync_bi, max_tau=max_tau)
     return _generic_distance_matrix(spike_trains, dist_func,
                                     indices, interval)

test/test_generic_interfaces.py

@@ -1,4 +1,4 @@
-""" test_isi_interface.py
+""" test_generic_interface.py
 
 Tests the generic interfaces of the profile and distance functions
 
@@ -88,6 +88,18 @@ def test_spike_distance():
     check_func(spk.spike_distance)
 
 
+def test_spike_sync_profile():
+    check_func(dist_from_prof(spk.spike_sync_profile))
+
+
+def test_spike_sync():
+    check_func(spk.spike_sync)
+
+
 if __name__ == "__main__":
     test_isi_profile()
     test_isi_distance()
+    test_spike_profile()
+    test_spike_distance()
+    test_spike_sync_profile()
+    test_spike_sync()