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Diffstat (limited to 'doc/tutorial.rst')
| -rw-r--r-- | doc/tutorial.rst | 54 |
1 files changed, 37 insertions, 17 deletions
diff --git a/doc/tutorial.rst b/doc/tutorial.rst index f7fc20b..aff03a8 100644 --- a/doc/tutorial.rst +++ b/doc/tutorial.rst @@ -88,10 +88,9 @@ If you are only interested in the scalar ISI-distance and not the profile, you c .. code:: python - isi_dist = spk.isi_distance(spike_trains[0], spike_trains[1], interval) - -where :code:`interval` is optional, as above, and if omitted the ISI-distance is computed for the complete spike trains. + isi_dist = spk.isi_distance(spike_trains[0], spike_trains[1], interval=(0, 1000)) +where :code:`interval` is optional, as above, and if omitted the ISI-distance is computed for the complete spike train. SPIKE-distance .............. @@ -113,19 +112,20 @@ But the general approach is very similar: plt.show() This short example computes and plots the SPIKE-profile of the first two spike trains in the file :code:`PySpike_testdata.txt`. + In contrast to the ISI-profile, a SPIKE-profile is a piece-wise *linear* function and is therefore represented by a :class:`.PieceWiseLinFunc` object. Just like the :class:`.PieceWiseConstFunc` for the ISI-profile, the :class:`.PieceWiseLinFunc` provides a :meth:`.PieceWiseLinFunc.get_plottable_data` member function that returns arrays that can be used directly to plot the function. Furthermore, the :meth:`.PieceWiseLinFunc.avrg` member function returns the average of the profile defined as the overall SPIKE distance. As above, you can provide an interval as a pair of floats as well as a sequence of such pairs to :code:`avrg` to specify the averaging interval if required. -Again, you can use +Again, you can use: .. code:: python - spike_dist = spk.spike_distance(spike_trains[0], spike_trains[1], interval) + spike_dist = spk.spike_distance(spike_trains[0], spike_trains[1], interval=ival) to compute the SPIKE distance directly, if you are not interested in the profile at all. -The parameter :code:`interval` is optional and if neglected the whole spike train is used. +The parameter :code:`interval` is optional and if neglected the whole time interval is used. SPIKE synchronization @@ -164,26 +164,47 @@ For the direct computation of the overall spike synchronization value within som .. code:: python - spike_sync = spk.spike_sync(spike_trains[0], spike_trains[1], interval) - + spike_sync = spk.spike_sync(spike_trains[0], spike_trains[1], interval=ival) Computing multivariate profiles and distances ---------------------------------------------- -To compute the multivariate ISI-profile, SPIKE-profile or SPIKE-Synchronization profile f a set of spike trains, PySpike provides multi-variate version of the profile function. -The following example computes the multivariate ISI-, SPIKE- and SPIKE-Sync-profile for a list of spike trains using the :func:`.isi_profile_multi`, :func:`.spike_profile_multi`, :func:`.spike_sync_profile_multi` functions: +To compute the multivariate ISI-profile, SPIKE-profile or SPIKE-Synchronization profile for a set of spike trains, simply provide a list of spike trains to the profile or distance functions. +The following example computes the multivariate ISI-, SPIKE- and SPIKE-Sync-profile for a list of spike trains: .. code:: python spike_trains = spk.load_spike_trains_from_txt("PySpike_testdata.txt", edges=(0, 4000)) - avrg_isi_profile = spk.isi_profile_multi(spike_trains) - avrg_spike_profile = spk.spike_profile_multi(spike_trains) - avrg_spike_sync_profile = spk.spike_sync_profile_multi(spike_trains) + avrg_isi_profile = spk.isi_profile(spike_trains) + avrg_spike_profile = spk.spike_profile(spike_trains) + avrg_spike_sync_profile = spk.spike_sync_profile(spike_trains) + +All functions also take an optional parameter :code:`indices`, a list of indices that allows to define the spike trains that should be used for the multivariate profile. +As before, if you are only interested in the distance values, and not in the profile, you can call the functions: :func:`.isi_distance`, :func:`.spike_distance` and :func:`.spike_sync` with a list of spike trains. +They return the scalar overall multivariate ISI-, SPIKE-distance or the SPIKE-Synchronization value. + +The following code is equivalent to the bivariate example above, computing the ISI-Distance between the first two spike trains in the given interval using the :code:`indices` parameter: + +.. code:: python + + isi_dist = spk.isi_distance(spike_trains, indices=[0, 1], interval=(0, 1000)) + +As you can see, the distance functions also accept an :code:`interval` parameter that can be used to specify the begin and end of the averaging interval as a pair of floats, if neglected the complete interval is used. + +**Note:** + +------------------------------ + + Instead of providing lists of spike trains to the profile or distance functions, you can also call those functions with many spike trains as (unnamed) parameters, e.g.: + + .. code:: python + + # st1, st2, st3, st4 are spike trains + spike_prof = spk.spike_profile(st1, st2, st3, st4) + +------------------------------ -All functions take an optional parameter :code:`indices`, a list of indices that allows to define the spike trains that should be used for the multivariate profile. -As before, if you are only interested in the distance values, and not in the profile, PySpike offers the functions: :func:`.isi_distance_multi`, :func:`.spike_distance_multi` and :func:`.spike_sync_multi`, that return the scalar overall multivariate ISI- and SPIKE-distance as well as the SPIKE-Synchronization value. -Those functions also accept an :code:`interval` parameter that can be used to specify the begin and end of the averaging interval as a pair of floats, if neglected the complete interval is used. Another option to characterize large sets of spike trains are distance matrices. Each entry in the distance matrix represents a bivariate distance (similarity for SPIKE-Synchronization) of two spike trains. @@ -210,4 +231,3 @@ The following example computes and plots the ISI- and SPIKE-distance matrix as w plt.title("SPIKE-Sync") plt.show() - |