Spike Order support (#39)0.6.0

* reorganized directionality module

* further refactoring of directionality

* completed python directionality backend

* added SPIKE-Sync based filtering

new function filter_by_spike_sync removes spikes that have a multi-variate
Spike Sync value below some threshold

not yet fully tested, python backend missing.

* spike sync filtering, cython sim ann

Added function for filtering out events based on a threshold for the spike
sync values. Usefull for focusing on synchronous events during directionality
analysis.
Also added cython version of simulated annealing for performance.

* added coincidence single profile to python backend

missing function in python backend added, identified and fixed a bug in the
implementation as well

* updated test case to new spike sync behavior

* python3 fixes

* another python3 fix

* reorganized directionality module

* further refactoring of directionality

* completed python directionality backend

* added SPIKE-Sync based filtering

new function filter_by_spike_sync removes spikes that have a multi-variate
Spike Sync value below some threshold

not yet fully tested, python backend missing.

* spike sync filtering, cython sim ann

Added function for filtering out events based on a threshold for the spike
sync values. Usefull for focusing on synchronous events during directionality
analysis.
Also added cython version of simulated annealing for performance.

* added coincidence single profile to python backend

missing function in python backend added, identified and fixed a bug in the
implementation as well

* updated test case to new spike sync behavior

* python3 fixes

* another python3 fix

* Fix absolute imports in directionality measures

* remove commented code

* Add directionality to docs, bump version

* Clean up directionality module, add doxy.

* Remove debug print from tests

* Fix bug in calling Python backend

* Fix incorrect integrals in PieceWiseConstFunc (#36)

* Add (some currently failing) tests for PieceWiseConstFunc.integral

* Fix implementation of PieceWiseConstFunc.integral

Just by adding a special condition for when we are only taking an
integral "between" two edges of a PieceWiseConstFunc
All tests now pass.
Fixes #33.

* Add PieceWiseConstFunc.integral tests for ValueError

* Add testing bounds of integral
* Raise ValueError in function implementation

* Fix incorrect integrals in PieceWiseLinFunc (#38)

Integrals of piece-wise linear functions were incorrect if the
requested interval lies completely between two support points.
This has been fixed, and a unit test exercising this behavior
was added.

Fixes #38

* Add Spike Order example and Tutorial section

Adds an example computing spike order profile and the optimal
spike train order. Also adds a section on spike train order to the
tutorial.

1 files changed, 52 insertions, 0 deletions

diff --git a/examples/spike_train_order.py b/examples/spike_train_order.py
new file mode 100644
index 0000000..3a42472
--- /dev/null
+++ b/examples/spike_train_order.py
@@ -0,0 +1,52 @@
+import numpy as np
+from matplotlib import pyplot as plt
+import pyspike as spk
+
+
+st1 = spk.generate_poisson_spikes(1.0, [0, 20])
+st2 = spk.generate_poisson_spikes(1.0, [0, 20])
+
+d = spk.spike_directionality(st1, st2)
+
+print "Spike Directionality of two Poissonian spike trains:", d
+
+E = spk.spike_train_order_profile(st1, st2)
+
+plt.figure()
+x, y = E.get_plottable_data()
+plt.plot(x, y, '-ob')
+plt.ylim(-1.1, 1.1)
+plt.xlabel("t")
+plt.ylabel("E")
+plt.title("Spike Train Order Profile")
+
+
+###### Optimize spike train order of 20 Random spike trains #######
+
+M = 20
+
+spike_trains = [spk.generate_poisson_spikes(1.0, [0, 100]) for m in xrange(M)]
+
+F_init = spk.spike_train_order(spike_trains)
+
+print "Initial Synfire Indicator for 20 Poissonian spike trains:", F_init
+
+D_init = spk.spike_directionality_matrix(spike_trains)
+
+phi, _ = spk.optimal_spike_train_sorting(spike_trains)
+
+F_opt = spk.spike_train_order(spike_trains, indices=phi)
+
+print "Synfire Indicator of optimized spike train sorting:", F_opt
+
+D_opt = spk.permutate_matrix(D_init, phi)
+
+plt.figure()
+plt.imshow(D_init)
+plt.title("Initial Directionality Matrix")
+
+plt.figure()
+plt.imshow(D_opt)
+plt.title("Optimized Directionality Matrix")
+
+plt.show()