more docs

3 files changed, 30 insertions, 5 deletions

diff --git a/Readme.md b/Readme.md
index a1b6e9e..55dbd33 100644
--- a/Readme.md
+++ b/Readme.md
@@ -64,6 +64,12 @@ If the time interval is provided (`time_interval is not None`), auxiliary spikes
 Furthermore, the spike trains are ordered via `np.sort` (disable this feature by providing `sort=False` as a parameter to the load function).

 As result, `load_spike_trains_from_txt` returns a *list of arrays* containing the spike trains in the text file.

 

+If you load spike trains yourself, i.e. from data files with different structure, you can use the helper function `add_auxiliary_spikes` to add the auxiliary spikes at the beginning and end of the observation interval.

+Both the ISI and the SPIKE distance computation require the presence of auxiliary spikes, so make sure you have those in your spike trains:

+

+    spike_train = spk.add_auxiliary_spikes(spike_train, (T_start, T_end))

+    # you provide only a single value, it is interpreted as T_end, while T_start=0

+    spike_train = spk.add_auxiliary_spikes(spike_train, T_end)

 

 ## Computing bi-variate distances

 

@@ -74,9 +80,30 @@ As result, `load_spike_trains_from_txt` returns a *list of arrays* containing th
 >For performance reasons, the PySpike distance functions do not check if the spike trains provided are indeed ordered.

 >Make sure that all your spike trains are ordered.

 >If in doubt, use `spike_train = np.sort(spike_train)` to obtain a correctly ordered spike train.

+>

+>Furthermore, the spike trains should have auxiliary spikes at the beginning and end of the observation interval.

+>You can ensure this by providing the `time_interval` in the `load_spike_trains_from_txt` function, or calling `add_auxiliary_spikes` for your spike trains.

+>The spike trains must have *the same* observation interval!

 

 ----------------------

 

+### ISI-distance

+

+The following code loads some exemplary spike trains, computes the dissimilarity profile ISI-distance of the first two spike trains, and plots it with matplotlib:

+

+    import matplotlib.pyplot as plt

+    import pyspike as spk

+    

+    spike_trains = spk.load_spike_trains_from_txt("PySpike_testdata.txt",

+                                                  time_interval=(0, 4000))

+    isi_profile = spk.isi_distance(spike_trains[0], spike_trains[1])

+    x, y = isi_profile.get_plottable_data()

+    plt.plot(x, np.abs(y), '--k')

+    print("ISI distance: %.8f" % isi_profil.abs_avrg())

+    plt.show()

+

+The ISI-profile is a piece-wise constant function, there the function `isi_distance` returns an instance of the `PieceWiseConstFunc` class.

+As above, this class allows you to obtain arrays that can be used to plot the function with `plt.plt`, but also to compute the absolute average, which amounts to the final scalar ISI-distance.

 

 ## Computing multi-variate distances

 

diff --git a/pyspike/spikes.py b/pyspike/spikes.py
index d390222..68c8bc1 100644
--- a/pyspike/spikes.py
+++ b/pyspike/spikes.py
@@ -20,7 +20,8 @@ def add_auxiliary_spikes(spike_train, time_interval):
     - time_interval: 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. Auxiliary spikes will be
-    added to the spike train at the beginning and end of this interval.
+    added to the spike train at the beginning and end of this interval, if they
+    are not yet present.
     Returns:
     - spike train with additional spikes at T_start and T_end.
 
diff --git a/setup.py b/setup.py
index 7c8e4e1..16a87ea 100644
--- a/setup.py
+++ b/setup.py
@@ -5,12 +5,9 @@ Handles the compilation of pyx source files
 Copyright 2014, Mario Mulansky <mario.mulansky@gmx.net>
 
 """
-import os
 from distutils.core import setup
 from Cython.Build import cythonize
 
-import numpy.distutils.intelccompiler
-
 setup(
-    ext_modules = cythonize("pyspike/*.pyx")
+    ext_modules=cythonize("pyspike/*.pyx")
 )