generalized interface to isi profile and distance

isi profile and distance functionc an now compute bi-variate and multi-variate
results. Therefore, it can be called with different "overloads".

pyspike/isi_distance.py

@@ -13,11 +13,40 @@
 ############################################################
 # isi_profile
 ############################################################
-def isi_profile(spike_train1, spike_train2):
+def isi_profile(*args, **kwargs):
     """ Computes the isi-distance profile :math:`I(t)` of the two given
-    spike trains. Retruns the profile as a PieceWiseConstFunc object. The
+    spike trains. Returns the profile as a PieceWiseConstFunc object. The
     ISI-values are defined positive :math:`I(t)>=0`.
 
+    Valid call structures::
+
+      isi_profile(st1, st2)  # returns the bi-variate profile
+      isi_profile(st1, st2, st3)  # multi-variate profile of 3 spike trains
+      spike_trains = [st1, st2, st3, st4]  # list of spike trains
+      isi_profile(spike_trains)  # return the profile the list of spike trains
+      isi_profile(spike_trains, indices=[0, 1])  # use only the spike trains
+                                                 # given by the indices
+
+    :returns: The isi-distance profile :math:`I(t)`
+    :rtype: :class:`.PieceWiseConstFunc`
+    """
+    if len(args) == 1:
+        return isi_profile_multi(args[0], **kwargs)
+    elif len(args) == 2:
+        return isi_profile_bi(args[0], args[1])
+    else:
+        return isi_profile_multi(args)
+
+
+############################################################
+# isi_profile_bi
+############################################################
+def isi_profile_bi(spike_train1, spike_train2):
+    """ Bi-variate ISI-profile.
+    Computes the isi-distance profile :math:`I(t)` of the two given
+    spike trains. Returns the profile as a PieceWiseConstFunc object.
+    See :func:`.isi_profile`.
+
     :param spike_train1: First spike train.
     :type spike_train1: :class:`.SpikeTrain`
     :param spike_train2: Second spike train.
@@ -51,10 +80,57 @@ def isi_profile(spike_train1, spike_train2):
     return PieceWiseConstFunc(times, values)
 
 
+############################################################
+# isi_profile_multi
+############################################################
+def isi_profile_multi(spike_trains, indices=None):
+    """ computes the multi-variate isi distance profile for a set of spike
+    trains. That is the average isi-distance of all pairs of spike-trains:
+
+    .. math:: <I(t)> = \\frac{2}{N(N-1)} \\sum_{<i,j>} I^{i,j},
+
+    where the sum goes over all pairs <i,j>
+
+    :param spike_trains: list of :class:`.SpikeTrain`
+    :param indices: list of indices defining which spike trains to use,
+                    if None all given spike trains are used (default=None)
+    :type state: list or None
+    :returns: The averaged isi profile :math:`<I(t)>`
+    :rtype: :class:`.PieceWiseConstFunc`
+    """
+    average_dist, M = _generic_profile_multi(spike_trains, isi_profile_bi,
+                                             indices)
+    average_dist.mul_scalar(1.0/M)  # normalize
+    return average_dist
+
+
 ############################################################
 # isi_distance
 ############################################################
-def isi_distance(spike_train1, spike_train2, interval=None):
+def isi_distance(*args, **kwargs):
+    # spike_trains, spike_train2, interval=None):
+    """ Computes the ISI-distance :math:`D_I` of the given spike trains. The
+    isi-distance is the integral over the isi distance profile
+    :math:`I(t)`:
+
+    .. math:: D_I = \\int_{T_0}^{T_1} I(t) dt.
+
+    :returns: The isi-distance :math:`D_I`.
+    :rtype: double
+    """
+
+    if len(args) == 1:
+        return isi_distance_multi(args[0], **kwargs)
+    elif len(args) == 2:
+        return isi_distance_bi(args[0], args[1], **kwargs)
+    else:
+        return isi_distance_multi(args, **kwargs)
+
+
+############################################################
+# isi_distance_bi
+############################################################
+def isi_distance_bi(spike_train1, spike_train2, interval=None):
     """ Computes the ISI-distance :math:`D_I` of the given spike trains. The
     isi-distance is the integral over the isi distance profile
     :math:`I(t)`:
@@ -84,34 +160,10 @@ def isi_distance(spike_train1, spike_train2, interval=None):
                                      spike_train1.t_start, spike_train1.t_end)
         except ImportError:
             # Cython backend not available: fall back to profile averaging
-            return isi_profile(spike_train1, spike_train2).avrg(interval)
+            return isi_profile_bi(spike_train1, spike_train2).avrg(interval)
     else:
         # some specific interval is provided: use profile
-        return isi_profile(spike_train1, spike_train2).avrg(interval)
-
-
-############################################################
-# isi_profile_multi
-############################################################
-def isi_profile_multi(spike_trains, indices=None):
-    """ computes the multi-variate isi distance profile for a set of spike
-    trains. That is the average isi-distance of all pairs of spike-trains:
-
-    .. math:: <I(t)> = \\frac{2}{N(N-1)} \\sum_{<i,j>} I^{i,j},
-
-    where the sum goes over all pairs <i,j>
-
-    :param spike_trains: list of :class:`.SpikeTrain`
-    :param indices: list of indices defining which spike trains to use,
-                    if None all given spike trains are used (default=None)
-    :type state: list or None
-    :returns: The averaged isi profile :math:`<I(t)>`
-    :rtype: :class:`.PieceWiseConstFunc`
-    """
-    average_dist, M = _generic_profile_multi(spike_trains, isi_profile,
-                                             indices)
-    average_dist.mul_scalar(1.0/M)  # normalize
-    return average_dist
+        return isi_profile_bi(spike_train1, spike_train2).avrg(interval)
 
 
 ############################################################
@@ -134,7 +186,7 @@ def isi_distance_multi(spike_trains, indices=None, interval=None):
     :returns: The time-averaged multivariate ISI distance :math:`D_I`
     :rtype: double
     """
-    return _generic_distance_multi(spike_trains, isi_distance, indices,
+    return _generic_distance_multi(spike_trains, isi_distance_bi, indices,
                                    interval)
 
 
@@ -155,5 +207,5 @@ def isi_distance_matrix(spike_trains, indices=None, interval=None):
               :math:`D_{I}^{ij}`
     :rtype: np.array
     """
-    return _generic_distance_matrix(spike_trains, isi_distance,
-                                    indices, interval)
+    return _generic_distance_matrix(spike_trains, isi_distance_bi,
+                                    indices=indices, interval=interval)

test/test_generic_interfaces.py

@@ -0,0 +1,64 @@
+""" test_isi_interface.py
+
+Tests the generic interfaces of the profile and distance functions
+
+Copyright 2016, Mario Mulansky <mario.mulansky@gmx.net>
+
+Distributed under the BSD License
+
+"""
+
+from __future__ import print_function
+from numpy.testing import assert_equal
+
+import pyspike as spk
+from pyspike import SpikeTrain
+
+
+class dist_from_prof:
+    """ Simple functor that turns profile function into distance function by
+    calling profile.avrg().
+    """
+    def __init__(self, prof_func):
+        self.prof_func = prof_func
+
+    def __call__(self, *args, **kwargs):
+        return self.prof_func(*args, **kwargs).avrg()
+
+
+def check_func(dist_func):
+    """ generic checker that tests the given distance function.
+    """
+    # generate spike trains:
+    t1 = SpikeTrain([0.2, 0.4, 0.6, 0.7], 1.0)
+    t2 = SpikeTrain([0.3, 0.45, 0.8, 0.9, 0.95], 1.0)
+    t3 = SpikeTrain([0.2, 0.4, 0.6], 1.0)
+    t4 = SpikeTrain([0.1, 0.4, 0.5, 0.6], 1.0)
+    spike_trains = [t1, t2, t3, t4]
+
+    isi12 = dist_func(t1, t2)
+    isi12_ = dist_func([t1, t2])
+    assert_equal(isi12, isi12_)
+
+    isi12_ = dist_func(spike_trains, indices=[0, 1])
+    assert_equal(isi12, isi12_)
+
+    isi123 = dist_func(t1, t2, t3)
+    isi123_ = dist_func([t1, t2, t3])
+    assert_equal(isi123, isi123_)
+
+    isi123_ = dist_func(spike_trains, indices=[0, 1, 2])
+    assert_equal(isi123, isi123_)
+
+
+def test_isi_profile():
+    check_func(dist_from_prof(spk.isi_profile))
+
+
+def test_isi_distance():
+    check_func(spk.isi_distance)
+
+
+if __name__ == "__main__":
+    test_isi_profile()
+    test_isi_distance()