diff options
Diffstat (limited to 'Readme.md')
| -rw-r--r-- | Readme.md | 12 |
1 files changed, 10 insertions, 2 deletions
@@ -98,13 +98,21 @@ The following code loads some exemplary spike trains, computes the dissimilarity 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.plot(x, y, '--k')
+ print("ISI distance: %.8f" % isi_profil.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.
+Furthermore, `PieceWiseConstFunc` provides an `add` function that can be used to add piece-wise constant function, and a `mul_scalar` function that rescales the function by a scalar.
+This can be used to obtain an average profile:
+
+ isi_profile1.add(isi_profile2)
+ isi_profile1.mul_scalar(0.5)
+ x, y = isi_profile1.get_plottable_data()
+ plt.plot(x, y, label="Average ISI profile")
+
## Computing multi-variate distances
|