Merge branch 'import_time_series' into develop

Conflicts:
	pyspike/__init__.py
resolved

5 files changed, 359 insertions, 125 deletions

diff --git a/pyspike/__init__.py b/pyspike/__init__.py
index 4d75786..08253fb 100644
--- a/pyspike/__init__.py
+++ b/pyspike/__init__.py
@@ -24,7 +24,8 @@ from .spike_sync import spike_sync_profile, spike_sync,\
 from .psth import psth
 
 from .spikes import load_spike_trains_from_txt, save_spike_trains_to_txt, \
-    spike_train_from_string, merge_spike_trains, generate_poisson_spikes
+    spike_train_from_string, import_spike_trains_from_time_series, \
+    merge_spike_trains, generate_poisson_spikes
 
 # define the __version__ following
 # http://stackoverflow.com/questions/17583443
diff --git a/pyspike/isi_distance.py b/pyspike/isi_distance.py
index 0ae7393..e91dce2 100644
--- a/pyspike/isi_distance.py
+++ b/pyspike/isi_distance.py
@@ -13,11 +13,48 @@ from pyspike.generic import _generic_profile_multi, _generic_distance_multi, \
 ############################################################
 # isi_profile
 ############################################################
-def isi_profile(spike_train1, spike_train2):
-    """ Computes the isi-distance profile :math:`I(t)` of the two given
-    spike trains. Retruns the profile as a PieceWiseConstFunc object. The
+def isi_profile(*args, **kwargs):
+    """ Computes the isi-distance profile :math:`I(t)` of the given
+    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)   # profile of the list of spike trains
+      isi_profile(spike_trains, indices=[0, 1])  # use only the spike trains
+                                                 # given by the indices
+
+    The multivariate ISI distance profile for a set of spike trains is defined
+    as the average ISI-profile 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>
+
+
+    :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):
+    """ Specific function to compute a bivariate ISI-profile. This is a
+    deprecated function and should not be called directly. Use
+    :func:`.isi_profile` to compute ISI-profiles.
+
     :param spike_train1: First spike train.
     :type spike_train1: :class:`.SpikeTrain`
     :param spike_train2: Second spike train.
@@ -52,15 +89,76 @@ Falling back to slow python backend.")
 
 
 ############################################################
+# isi_profile_multi
+############################################################
+def isi_profile_multi(spike_trains, indices=None):
+    """ Specific function to compute the multivariate ISI-profile for a set of
+    spike trains. This is a deprecated function and should not be called
+    directly. Use :func:`.isi_profile` to compute ISI-profiles.
+
+
+    :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):
     """ 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.
 
+    In the multivariate case it is the integral over the multivariate
+    ISI-profile, i.e. the average profile over all spike train pairs:
+
+    .. math:: D_I = \\int_0^T \\frac{2}{N(N-1)} \\sum_{<i,j>} I^{i,j},
+
+    where the sum goes over all pairs <i,j>
+
+
+
+    Valid call structures::
+
+      isi_distance(st1, st2)  # returns the bi-variate distance
+      isi_distance(st1, st2, st3)  # multi-variate distance of 3 spike trains
+
+      spike_trains = [st1, st2, st3, st4]  # list of spike trains
+      isi_distance(spike_trains)  # distance of the list of spike trains
+      isi_distance(spike_trains, indices=[0, 1])  # use only the spike trains
+                                                  # given by the indices
+
+    :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):
+    """ Specific function to compute the bivariate ISI-distance.
+    This is a deprecated function and should not be called directly. Use
+    :func:`.isi_distance` to compute ISI-distances.
+
     :param spike_train1: First spike train.
     :type spike_train1: :class:`.SpikeTrain`
     :param spike_train2: Second spike train.
@@ -84,46 +182,19 @@ 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)
 
 
 ############################################################
 # isi_distance_multi
 ############################################################
 def isi_distance_multi(spike_trains, indices=None, interval=None):
-    """ computes the multi-variate isi-distance for a set of spike-trains.
-    That is the time average of the multi-variate spike profile:
-
-    .. math:: D_I = \\int_0^T \\frac{2}{N(N-1)} \\sum_{<i,j>} I^{i,j},
-
-    where the sum goes over all pairs <i,j>
+    """ Specific function to compute the multivariate ISI-distance.
+    This is a deprecfated function and should not be called directly. Use
+    :func:`.isi_distance` to compute ISI-distances.
 
     :param spike_trains: list of :class:`.SpikeTrain`
     :param indices: list of indices defining which spike trains to use,
@@ -134,7 +205,7 @@ 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 +226,5 @@ 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)
diff --git a/pyspike/spike_distance.py b/pyspike/spike_distance.py
index e418283..0fd86c1 100644
--- a/pyspike/spike_distance.py
+++ b/pyspike/spike_distance.py
@@ -13,10 +13,46 @@ from pyspike.generic import _generic_profile_multi, _generic_distance_multi, \
 ############################################################
 # spike_profile
 ############################################################
-def spike_profile(spike_train1, spike_train2):
-    """ Computes the spike-distance profile :math:`S(t)` of the two given spike
-    trains. Returns the profile as a PieceWiseLinFunc object. The SPIKE-values
-    are defined positive :math:`S(t)>=0`.
+def spike_profile(*args, **kwargs):
+    """ Computes the spike-distance profile :math:`S(t)` of the given
+    spike trains. Returns the profile as a PieceWiseConstLin object. The
+    SPIKE-values are defined positive :math:`S(t)>=0`.
+
+    Valid call structures::
+
+      spike_profile(st1, st2)  # returns the bi-variate profile
+      spike_profile(st1, st2, st3)  # multi-variate profile of 3 spike trains
+
+      spike_trains = [st1, st2, st3, st4]  # list of spike trains
+      spike_profile(spike_trains)  # profile of the list of spike trains
+      spike_profile(spike_trains, indices=[0, 1])  # use only the spike trains
+                                                   # given by the indices
+
+    The multivariate spike-distance profile is defined as the average of all
+    pairs of spike-trains:
+
+    .. math:: <S(t)> = \\frac{2}{N(N-1)} \\sum_{<i,j>} S^{i, j}`,
+
+    where the sum goes over all pairs <i,j>
+
+    :returns: The spike-distance profile :math:`S(t)`
+    :rtype: :class:`.PieceWiseConstLin`
+    """
+    if len(args) == 1:
+        return spike_profile_multi(args[0], **kwargs)
+    elif len(args) == 2:
+        return spike_profile_bi(args[0], args[1])
+    else:
+        return spike_profile_multi(args)
+
+
+############################################################
+# spike_profile_bi
+############################################################
+def spike_profile_bi(spike_train1, spike_train2):
+    """ Specific function to compute a bivariate SPIKE-profile. This is a
+    deprecated function and should not be called directly. Use
+    :func:`.spike_profile` to compute SPIKE-profiles.
 
     :param spike_train1: First spike train.
     :type spike_train1: :class:`.SpikeTrain`
@@ -54,14 +90,74 @@ Falling back to slow python backend.")
 
 
 ############################################################
+# spike_profile_multi
+############################################################
+def spike_profile_multi(spike_trains, indices=None):
+    """ Specific function to compute a multivariate SPIKE-profile. This is a
+    deprecated function and should not be called directly. Use
+    :func:`.spike_profile` to compute SPIKE-profiles.
+
+    :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 indices: list or None
+    :returns: The averaged spike profile :math:`<S>(t)`
+    :rtype: :class:`.PieceWiseLinFunc`
+
+    """
+    average_dist, M = _generic_profile_multi(spike_trains, spike_profile_bi,
+                                             indices)
+    average_dist.mul_scalar(1.0/M)  # normalize
+    return average_dist
+
+
+############################################################
 # spike_distance
 ############################################################
-def spike_distance(spike_train1, spike_train2, interval=None):
-    """ Computes the spike-distance :math:`D_S` of the given spike trains. The
+def spike_distance(*args, **kwargs):
+    """ Computes the SPIKE-distance :math:`D_S` of the given spike trains. The
     spike-distance is the integral over the spike distance profile
-    :math:`S(t)`:
+    :math:`D(t)`:
+
+    .. math:: D_S = \\int_{T_0}^{T_1} S(t) dt.
+
 
-    .. math:: D_S = \int_{T_0}^{T_1} S(t) dt.
+    Valid call structures::
+
+      spike_distance(st1, st2)  # returns the bi-variate distance
+      spike_distance(st1, st2, st3)  # multi-variate distance of 3 spike trains
+
+      spike_trains = [st1, st2, st3, st4]  # list of spike trains
+      spike_distance(spike_trains)  # distance of the list of spike trains
+      spike_distance(spike_trains, indices=[0, 1])  # use only the spike trains
+                                                    # given by the indices
+
+    In the multivariate case, the spike distance is given as the integral over
+    the multivariate profile, that is the average profile of all spike train
+    pairs:
+
+    .. math::  D_S = \\int_0^T \\frac{2}{N(N-1)} \\sum_{<i,j>}
+               S^{i, j} dt
+
+    :returns: The spike-distance :math:`D_S`.
+    :rtype: double
+    """
+
+    if len(args) == 1:
+        return spike_distance_multi(args[0], **kwargs)
+    elif len(args) == 2:
+        return spike_distance_bi(args[0], args[1], **kwargs)
+    else:
+        return spike_distance_multi(args, **kwargs)
+
+
+############################################################
+# spike_distance_bi
+############################################################
+def spike_distance_bi(spike_train1, spike_train2, interval=None):
+    """ Specific function to compute a bivariate SPIKE-distance. This is a
+    deprecated function and should not be called directly. Use
+    :func:`.spike_distance` to compute SPIKE-distances.
 
     :param spike_train1: First spike train.
     :type spike_train1: :class:`.SpikeTrain`
@@ -86,48 +182,19 @@ def spike_distance(spike_train1, spike_train2, interval=None):
                                        spike_train1.t_end)
         except ImportError:
             # Cython backend not available: fall back to average profile
-            return spike_profile(spike_train1, spike_train2).avrg(interval)
+            return spike_profile_bi(spike_train1, spike_train2).avrg(interval)
     else:
         # some specific interval is provided: compute the whole profile
-        return spike_profile(spike_train1, spike_train2).avrg(interval)
-
-
-############################################################
-# spike_profile_multi
-############################################################
-def spike_profile_multi(spike_trains, indices=None):
-    """ Computes the multi-variate spike distance profile for a set of spike
-    trains. That is the average spike-distance of all pairs of spike-trains:
-
-    .. math:: <S(t)> = \\frac{2}{N(N-1)} \\sum_{<i,j>} S^{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 indices: list or None
-    :returns: The averaged spike profile :math:`<S>(t)`
-    :rtype: :class:`.PieceWiseLinFunc`
-
-    """
-    average_dist, M = _generic_profile_multi(spike_trains, spike_profile,
-                                             indices)
-    average_dist.mul_scalar(1.0/M)  # normalize
-    return average_dist
+        return spike_profile_bi(spike_train1, spike_train2).avrg(interval)
 
 
 ############################################################
 # spike_distance_multi
 ############################################################
 def spike_distance_multi(spike_trains, indices=None, interval=None):
-    """ Computes the multi-variate spike distance for a set of spike trains.
-    That is the time average of the multi-variate spike profile:
-
-    .. math::  D_S = \\int_0^T \\frac{2}{N(N-1)} \\sum_{<i,j>}
-               S^{i, j} dt
-
-    where the sum goes over all pairs <i,j>
+    """ Specific function to compute a multivariate SPIKE-distance. This is a
+    deprecated function and should not be called directly. Use
+    :func:`.spike_distance` to compute SPIKE-distances.
 
     :param spike_trains: list of :class:`.SpikeTrain`
     :param indices: list of indices defining which spike trains to use,
@@ -139,7 +206,7 @@ def spike_distance_multi(spike_trains, indices=None, interval=None):
     :returns: The averaged multi-variate spike distance :math:`D_S`.
     :rtype: double
     """
-    return _generic_distance_multi(spike_trains, spike_distance, indices,
+    return _generic_distance_multi(spike_trains, spike_distance_bi, indices,
                                    interval)
 
 
@@ -160,5 +227,5 @@ def spike_distance_matrix(spike_trains, indices=None, interval=None):
               :math:`D_S^{ij}`
     :rtype: np.array
     """
-    return _generic_distance_matrix(spike_trains, spike_distance,
+    return _generic_distance_matrix(spike_trains, spike_distance_bi,
                                     indices, interval)
diff --git a/pyspike/spike_sync.py b/pyspike/spike_sync.py
index 802b98c..80f7805 100644
--- a/pyspike/spike_sync.py
+++ b/pyspike/spike_sync.py
@@ -15,11 +15,48 @@ from pyspike.generic import _generic_profile_multi, _generic_distance_matrix
 ############################################################
 # spike_sync_profile
 ############################################################
-def spike_sync_profile(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
-    coincidence.
+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
+
+    In the multivariate case, the profile is defined as the number of
+    coincidences for each spike in the set of spike trains 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).
+
+    :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):
+    """ Specific function to compute a bivariate SPIKE-Sync-profile. This is a
+    deprecated function and should not be called directly. Use
+    :func:`.spike_sync_profile` to compute SPIKE-Sync-profiles.
 
     :param spike_train1: First spike train.
     :type spike_train1: :class:`pyspike.SpikeTrain`
@@ -27,7 +64,7 @@ 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`
 
     """
@@ -62,6 +99,31 @@ Falling back to slow python backend.")
 
 
 ############################################################
+# spike_sync_profile_multi
+############################################################
+def spike_sync_profile_multi(spike_trains, indices=None, max_tau=None):
+    """  Specific function to compute a multivariate SPIKE-Sync-profile.
+    This is a deprecated function and should not be called directly. Use
+    :func:`.spike_sync_profile` to compute SPIKE-Sync-profiles.
+
+    :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
 ############################################################
 def _spike_sync_values(spike_train1, spike_train2, interval, max_tau):
@@ -87,24 +149,58 @@ 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
+
+    The multivariate SPIKE-Sync is again defined as the overall ratio of all
+    coincidence values divided by the total number of spikes.
+
+    :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):
+    """ Specific function to compute a bivariate SPIKE-Sync value.
+    This is a deprecated function and should not be called directly. Use
+    :func:`.spike_sync` to compute SPIKE-Sync values.
+
     :param spike_train1: First spike train.
     :type spike_train1: :class:`pyspike.SpikeTrain`
     :param spike_train2: Second spike train.
@@ -126,38 +222,12 @@ def spike_sync(spike_train1, spike_train2, interval=None, max_tau=None):
 
 
 ############################################################
-# 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
 ############################################################
 def spike_sync_multi(spike_trains, indices=None, interval=None, max_tau=None):
-    """ Computes the multi-variate spike synchronization value for a set of
-    spike trains.
+    """ Specific function to compute a multivariate SPIKE-Sync value.
+    This is a deprecated function and should not be called directly. Use
+    :func:`.spike_sync` to compute SPIKE-Sync values.
 
     :param spike_trains: list of :class:`pyspike.SpikeTrain`
     :param indices: list of indices defining which spike trains to use,
@@ -217,6 +287,6 @@ 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)
diff --git a/pyspike/spikes.py b/pyspike/spikes.py
index ab6d4c4..486a4a0 100644
--- a/pyspike/spikes.py
+++ b/pyspike/spikes.py
@@ -62,6 +62,31 @@ def load_spike_trains_from_txt(file_name, edges,
     return spike_trains
 
 
+def import_spike_trains_from_time_series(file_name, start_time, time_bin,
+                                         separator=None, comment='#'):
+    """ Imports spike trains from time series consisting of 0 and 1 denoting
+    the absence or presence of a spike. Each line in the data file represents
+    one spike train.
+
+    :param file_name: The name of the data file containing the time series.
+    :param edges: A pair (T_start, T_end) of values representing the
+                  start and end time of the spike train measurement
+                  or a single value representing the end time, the
+                  T_start is then assuemd as 0.
+    :param separator: The character used to seprate the values in the text file
+    :param comment: Lines starting with this character are ignored.
+
+    """
+    data = np.loadtxt(file_name, comments=comment, delimiter=separator)
+    time_points = start_time + time_bin + np.arange(len(data[0, :]))*time_bin
+    spike_trains = []
+    for time_series in data:
+        spike_trains.append(SpikeTrain(time_points[time_series > 0],
+                                       edges=[start_time,
+                                              time_points[-1]]))
+    return spike_trains
+
+
 ############################################################
 # save_spike_trains_to_txt
 ############################################################