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, 62 insertions, 0 deletions

diff --git a/test/test_function.py b/test/test_function.py
index 92d378d..6c04839 100644
--- a/test/test_function.py
+++ b/test/test_function.py
@@ -10,6 +10,7 @@ Distributed under the BSD License
 from __future__ import print_function
 import numpy as np
 from copy import copy
+from nose.tools import raises
 from numpy.testing import assert_equal, assert_almost_equal, \
     assert_array_equal, assert_array_almost_equal
 
@@ -49,6 +50,8 @@ def test_pwc():
     assert_almost_equal(a, (0.5-0.5+0.5*1.5+1.0*0.75)/3.0, decimal=16)
     a = f.avrg([1.5, 3.5])
     assert_almost_equal(a, (-0.5*0.5+0.5*1.5+1.0*0.75)/2.0, decimal=16)
+    a = f.avrg([1.0, 2.0])
+    assert_almost_equal(a, (1.0*-0.5)/1.0, decimal=16)
     a = f.avrg([1.0, 3.5])
     assert_almost_equal(a, (-0.5*1.0+0.5*1.5+1.0*0.75)/2.5, decimal=16)
     a = f.avrg([1.0, 4.0])
@@ -120,6 +123,53 @@ def test_pwc_avrg():
     assert_array_almost_equal(f1.x, x_expected, decimal=16)
     assert_array_almost_equal(f1.y, y_expected, decimal=16)
 
+def test_pwc_integral():
+    # some random data
+    x = [0.0, 1.0, 2.0, 2.5, 4.0]
+    y = [1.0, -0.5, 1.5, 0.75]
+    f1 = spk.PieceWiseConstFunc(x, y)
+
+    # test full interval
+    full = 1.0*1.0 + 1.0*-0.5 + 0.5*1.5 + 1.5*0.75;
+    assert_equal(f1.integral(), full)
+    assert_equal(f1.integral((np.min(x),np.max(x))), full)
+    # test part interval, spanning an edge
+    assert_equal(f1.integral((0.5,1.5)), 0.5*1.0 + 0.5*-0.5)
+    # test part interval, just over two edges
+    assert_almost_equal(f1.integral((1.0-1e-16,2+1e-16)), 1.0*-0.5, decimal=14)
+    # test part interval, between two edges
+    assert_equal(f1.integral((1.0,2.0)), 1.0*-0.5)
+    assert_equal(f1.integral((1.2,1.7)), (1.7-1.2)*-0.5)
+    # test part interval, start to before and after edge
+    assert_equal(f1.integral((0.0,0.7)), 0.7*1.0)
+    assert_equal(f1.integral((0.0,1.1)), 1.0*1.0+0.1*-0.5)
+    # test part interval, before and after edge till end
+    assert_equal(f1.integral((2.6,4.0)), (4.0-2.6)*0.75)
+    assert_equal(f1.integral((2.4,4.0)), (2.5-2.4)*1.5+(4-2.5)*0.75)
+
+@raises(ValueError)
+def test_pwc_integral_bad_bounds_inv():
+    # some random data
+    x = [0.0, 1.0, 2.0, 2.5, 4.0]
+    y = [1.0, -0.5, 1.5, 0.75]
+    f1 = spk.PieceWiseConstFunc(x, y)
+    f1.integral((3,2))
+
+@raises(ValueError)
+def test_pwc_integral_bad_bounds_oob_1():
+    # some random data
+    x = [0.0, 1.0, 2.0, 2.5, 4.0]
+    y = [1.0, -0.5, 1.5, 0.75]
+    f1 = spk.PieceWiseConstFunc(x, y)
+    f1.integral((1,6))
+
+@raises(ValueError)
+def test_pwc_integral_bad_bounds_oob_2():
+    # some random data
+    x = [0.0, 1.0, 2.0, 2.5, 4.0]
+    y = [1.0, -0.5, 1.5, 0.75]
+    f1 = spk.PieceWiseConstFunc(x, y)
+    f1.integral((-1,3))
 
 def test_pwl():
     x = [0.0, 1.0, 2.0, 2.5, 4.0]
@@ -162,6 +212,18 @@ def test_pwl():
     a = f.avrg([1.0, 4.0])
     assert_almost_equal(a, (-0.45 + 0.75 + 1.5*0.5) / 3.0, decimal=16)
 
+    # interval between support points
+    a = f.avrg([1.1, 1.5])
+    assert_almost_equal(a, (-0.5+0.1*0.1 - 0.45) * 0.5, decimal=14)
+
+    # starting at a support point
+    a = f.avrg([1.0, 1.5])
+    assert_almost_equal(a, (-0.5 - 0.45) * 0.5, decimal=14)
+
+    # start and end at support point
+    a = f.avrg([1.0, 2.0])
+    assert_almost_equal(a, (-0.5 - 0.4) * 0.5, decimal=14)
+    
     # averaging over multiple intervals
     a = f.avrg([(0.5, 1.5), (1.5, 2.5)])
     assert_almost_equal(a, (1.375*0.5 - 0.45 + 0.75)/2.0, decimal=16)