Source code for elephant.test.test_spike_train_dissimilarity

# -*- coding: utf-8 -*-
"""
Tests for the spike train dissimilarity measures module.

:copyright: Copyright 2016 by the Elephant team, see AUTHORS.txt.
:license: Modified BSD, see LICENSE.txt for details.
"""

import unittest
from neo import SpikeTrain
import numpy as np
from numpy.testing import assert_array_equal, assert_array_almost_equal
import scipy.integrate as spint
from quantities import ms, s, Hz
import elephant.kernels as kernels
import elephant.spike_train_generation as stg
import elephant.spike_train_dissimilarity as stds

[docs]class TimeScaleDependSpikeTrainDissimMeasures_TestCase(unittest.TestCase):
[docs] def setUp(self): self.st00 = SpikeTrain([], units='ms', t_stop=1000.0) self.st01 = SpikeTrain([1], units='ms', t_stop=1000.0) self.st02 = SpikeTrain([2], units='ms', t_stop=1000.0) self.st03 = SpikeTrain([2.9], units='ms', t_stop=1000.0) self.st04 = SpikeTrain([3.1], units='ms', t_stop=1000.0) self.st05 = SpikeTrain([5], units='ms', t_stop=1000.0) self.st06 = SpikeTrain([500], units='ms', t_stop=1000.0) self.st07 = SpikeTrain([12, 32], units='ms', t_stop=1000.0) self.st08 = SpikeTrain([32, 52], units='ms', t_stop=1000.0) self.st09 = SpikeTrain([42], units='ms', t_stop=1000.0) self.st10 = SpikeTrain([18, 60], units='ms', t_stop=1000.0) self.st11 = SpikeTrain([10, 20, 30, 40], units='ms', t_stop=1000.0) self.st12 = SpikeTrain([40, 30, 20, 10], units='ms', t_stop=1000.0) self.st13 = SpikeTrain([15, 25, 35, 45], units='ms', t_stop=1000.0) self.st14 = SpikeTrain([10, 20, 30, 40, 50], units='ms', t_stop=1000.0) self.st15 = SpikeTrain([0.01, 0.02, 0.03, 0.04, 0.05], units='s', t_stop=1000.0) self.st16 = SpikeTrain([12, 16, 28, 30, 42], units='ms', t_stop=1000.0) self.st21 = stg.homogeneous_poisson_process(50*Hz, 0*ms, 1000*ms) self.st22 = stg.homogeneous_poisson_process(40*Hz, 0*ms, 1000*ms) self.st23 = stg.homogeneous_poisson_process(30*Hz, 0*ms, 1000*ms) self.rd_st_list = [self.st21, self.st22, self.st23] self.st31 = SpikeTrain([12.0], units='ms', t_stop=1000.0) self.st32 = SpikeTrain([12.0, 12.0], units='ms', t_stop=1000.0) self.st33 = SpikeTrain([20.0], units='ms', t_stop=1000.0) self.st34 = SpikeTrain([20.0, 20.0], units='ms', t_stop=1000.0) self.array1 = np.arange(1, 10) self.array2 = np.arange(1.2, 10) self.qarray1 = self.array1 * Hz self.qarray2 = self.array2 * Hz self.tau0 = 0.0 * ms self.q0 = np.inf / ms self.tau1 = 0.000000001 * ms self.q1 = 1.0 / self.tau1 self.tau2 = 1.0 * ms self.q2 = 1.0 / self.tau2 self.tau3 = 10.0 * ms self.q3 = 1.0 / self.tau3 self.tau4 = 100.0 * ms self.q4 = 1.0 / self.tau4 self.tau5 = 1000000000.0 * ms self.q5 = 1.0 / self.tau5 self.tau6 = np.inf * ms self.q6 = 0.0 / ms self.tau7 = 0.01 * s self.q7 = 1.0 / self.tau7 self.t = np.linspace(0, 200, 20000001) * ms
[docs] def test_wrong_input(self): self.assertRaises(TypeError, stds.victor_purpura_dist, [self.array1, self.array2], self.q3) self.assertRaises(TypeError, stds.victor_purpura_dist, [self.qarray1, self.qarray2], self.q3) self.assertRaises(TypeError, stds.victor_purpura_dist, [self.qarray1, self.qarray2], 5.0 * ms) self.assertRaises(TypeError, stds.victor_purpura_dist, [self.array1, self.array2], self.q3, algorithm='intuitive') self.assertRaises(TypeError, stds.victor_purpura_dist, [self.qarray1, self.qarray2], self.q3, algorithm='intuitive') self.assertRaises(TypeError, stds.victor_purpura_dist, [self.qarray1, self.qarray2], 5.0 * ms, algorithm='intuitive') self.assertRaises(TypeError, stds.van_rossum_dist, [self.array1, self.array2], self.tau3) self.assertRaises(TypeError, stds.van_rossum_dist, [self.qarray1, self.qarray2], self.tau3) self.assertRaises(TypeError, stds.van_rossum_dist, [self.qarray1, self.qarray2], 5.0 * Hz) self.assertRaises(TypeError, stds.victor_purpura_dist, [self.st11, self.st13], self.tau2) self.assertRaises(TypeError, stds.victor_purpura_dist, [self.st11, self.st13], 5.0) self.assertRaises(TypeError, stds.victor_purpura_dist, [self.st11, self.st13], self.tau2, algorithm='intuitive') self.assertRaises(TypeError, stds.victor_purpura_dist, [self.st11, self.st13], 5.0, algorithm='intuitive') self.assertRaises(TypeError, stds.van_rossum_dist, [self.st11, self.st13], self.q4) self.assertRaises(TypeError, stds.van_rossum_dist, [self.st11, self.st13], 5.0) self.assertRaises(NotImplementedError, stds.victor_purpura_dist, [self.st01, self.st02], self.q3, kernel=kernels.Kernel(2.0 / self.q3)) self.assertRaises(NotImplementedError, stds.victor_purpura_dist, [self.st01, self.st02], self.q3, kernel=kernels.SymmetricKernel(2.0 / self.q3)) self.assertEqual(stds.victor_purpura_dist( [self.st01, self.st02], self.q1, kernel=kernels.TriangularKernel( 2.0 / (np.sqrt(6.0) * self.q2)))[0, 1], stds.victor_purpura_dist( [self.st01, self.st02], self.q3, kernel=kernels.TriangularKernel( 2.0 / (np.sqrt(6.0) * self.q2)))[0, 1]) self.assertEqual(stds.victor_purpura_dist( [self.st01, self.st02], kernel=kernels.TriangularKernel( 2.0 / (np.sqrt(6.0) * self.q2)))[0, 1], 1.0) self.assertNotEqual(stds.victor_purpura_dist( [self.st01, self.st02], kernel=kernels.AlphaKernel( 2.0 / (np.sqrt(6.0) * self.q2)))[0, 1], 1.0) self.assertRaises(NameError, stds.victor_purpura_dist, [self.st11, self.st13], self.q2, algorithm='slow')
[docs] def test_victor_purpura_distance_fast(self): # Tests of distances of simplest spike trains: self.assertEqual(stds.victor_purpura_dist( [self.st00, self.st00], self.q2)[0, 1], 0.0) self.assertEqual(stds.victor_purpura_dist( [self.st00, self.st01], self.q2)[0, 1], 1.0) self.assertEqual(stds.victor_purpura_dist( [self.st01, self.st00], self.q2)[0, 1], 1.0) self.assertEqual(stds.victor_purpura_dist( [self.st01, self.st01], self.q2)[0, 1], 0.0) # Tests of distances under elementary spike operations self.assertEqual(stds.victor_purpura_dist( [self.st01, self.st02], self.q2)[0, 1], 1.0) self.assertEqual(stds.victor_purpura_dist( [self.st01, self.st03], self.q2)[0, 1], 1.9) self.assertEqual(stds.victor_purpura_dist( [self.st01, self.st04], self.q2)[0, 1], 2.0) self.assertEqual(stds.victor_purpura_dist( [self.st01, self.st05], self.q2)[0, 1], 2.0) self.assertEqual(stds.victor_purpura_dist( [self.st00, self.st07], self.q2)[0, 1], 2.0) self.assertAlmostEqual(stds.victor_purpura_dist( [self.st07, self.st08], self.q4)[0, 1], 0.4) self.assertAlmostEqual(stds.victor_purpura_dist( [self.st07, self.st10], self.q3)[0, 1], 0.6 + 2) self.assertEqual(stds.victor_purpura_dist( [self.st11, self.st14], self.q2)[0, 1], 1) # Tests on timescales self.assertEqual(stds.victor_purpura_dist( [self.st11, self.st14], self.q1)[0, 1], stds.victor_purpura_dist( [self.st11, self.st14], self.q5)[0, 1]) self.assertEqual(stds.victor_purpura_dist( [self.st07, self.st11], self.q0)[0, 1], 6.0) self.assertEqual(stds.victor_purpura_dist( [self.st07, self.st11], self.q1)[0, 1], 6.0) self.assertAlmostEqual(stds.victor_purpura_dist( [self.st07, self.st11], self.q5)[0, 1], 2.0, 5) self.assertEqual(stds.victor_purpura_dist( [self.st07, self.st11], self.q6)[0, 1], 2.0) # Tests on unordered spiketrains self.assertEqual(stds.victor_purpura_dist( [self.st11, self.st13], self.q4)[0, 1], stds.victor_purpura_dist( [self.st12, self.st13], self.q4)[0, 1]) self.assertNotEqual(stds.victor_purpura_dist( [self.st11, self.st13], self.q4, sort=False)[0, 1], stds.victor_purpura_dist( [self.st12, self.st13], self.q4, sort=False)[0, 1]) # Tests on metric properties with random spiketrains # (explicit calculation of second metric axiom in particular case, # because from dist_matrix it is trivial) dist_matrix = stds.victor_purpura_dist( [self.st21, self.st22, self.st23], self.q3) for i in range(3): for j in range(3): self.assertGreaterEqual(dist_matrix[i, j], 0) if dist_matrix[i, j] == 0: assert_array_equal(self.rd_st_list[i], self.rd_st_list[j]) assert_array_equal(stds.victor_purpura_dist( [self.st21, self.st22], self.q3), stds.victor_purpura_dist( [self.st22, self.st21], self.q3)) self.assertLessEqual(dist_matrix[0, 1], dist_matrix[0, 2] + dist_matrix[1, 2]) self.assertLessEqual(dist_matrix[0, 2], dist_matrix[1, 2] + dist_matrix[0, 1]) self.assertLessEqual(dist_matrix[1, 2], dist_matrix[0, 1] + dist_matrix[0, 2]) # Tests on proper unit conversion self.assertAlmostEqual( stds.victor_purpura_dist([self.st14, self.st16], self.q3)[0, 1], stds.victor_purpura_dist([self.st15, self.st16], self.q3)[0, 1]) self.assertAlmostEqual( stds.victor_purpura_dist([self.st16, self.st14], self.q3)[0, 1], stds.victor_purpura_dist([self.st16, self.st15], self.q3)[0, 1]) self.assertEqual( stds.victor_purpura_dist([self.st01, self.st05], self.q3)[0, 1], stds.victor_purpura_dist([self.st01, self.st05], self.q7)[0, 1]) # Tests on algorithmic behaviour for equal spike times self.assertEqual( stds.victor_purpura_dist([self.st31, self.st34], self.q3)[0, 1], 0.8 + 1.0) self.assertEqual( stds.victor_purpura_dist([self.st31, self.st34], self.q3)[0, 1], stds.victor_purpura_dist([self.st32, self.st33], self.q3)[0, 1]) self.assertEqual( stds.victor_purpura_dist( [self.st31, self.st33], self.q3)[0, 1] * 2.0, stds.victor_purpura_dist( [self.st32, self.st34], self.q3)[0, 1]) # Tests on spike train list lengthes smaller than 2 self.assertEqual(stds.victor_purpura_dist( [self.st21], self.q3)[0, 0], 0) self.assertEqual(len(stds.victor_purpura_dist([], self.q3)), 0)
[docs] def test_victor_purpura_distance_intuitive(self): # Tests of distances of simplest spike trains self.assertEqual(stds.victor_purpura_dist( [self.st00, self.st00], self.q2, algorithm='intuitive')[0, 1], 0.0) self.assertEqual(stds.victor_purpura_dist( [self.st00, self.st01], self.q2, algorithm='intuitive')[0, 1], 1.0) self.assertEqual(stds.victor_purpura_dist( [self.st01, self.st00], self.q2, algorithm='intuitive')[0, 1], 1.0) self.assertEqual(stds.victor_purpura_dist( [self.st01, self.st01], self.q2, algorithm='intuitive')[0, 1], 0.0) # Tests of distances under elementary spike operations self.assertEqual(stds.victor_purpura_dist( [self.st01, self.st02], self.q2, algorithm='intuitive')[0, 1], 1.0) self.assertEqual(stds.victor_purpura_dist( [self.st01, self.st03], self.q2, algorithm='intuitive')[0, 1], 1.9) self.assertEqual(stds.victor_purpura_dist( [self.st01, self.st04], self.q2, algorithm='intuitive')[0, 1], 2.0) self.assertEqual(stds.victor_purpura_dist( [self.st01, self.st05], self.q2, algorithm='intuitive')[0, 1], 2.0) self.assertEqual(stds.victor_purpura_dist( [self.st00, self.st07], self.q2, algorithm='intuitive')[0, 1], 2.0) self.assertAlmostEqual(stds.victor_purpura_dist( [self.st07, self.st08], self.q4, algorithm='intuitive')[0, 1], 0.4) self.assertAlmostEqual(stds.victor_purpura_dist( [self.st07, self.st10], self.q3, algorithm='intuitive')[0, 1], 2.6) self.assertEqual(stds.victor_purpura_dist( [self.st11, self.st14], self.q2, algorithm='intuitive')[0, 1], 1) # Tests on timescales self.assertEqual(stds.victor_purpura_dist( [self.st11, self.st14], self.q1, algorithm='intuitive')[0, 1], stds.victor_purpura_dist( [self.st11, self.st14], self.q5, algorithm='intuitive')[0, 1]) self.assertEqual(stds.victor_purpura_dist( [self.st07, self.st11], self.q0, algorithm='intuitive')[0, 1], 6.0) self.assertEqual(stds.victor_purpura_dist( [self.st07, self.st11], self.q1, algorithm='intuitive')[0, 1], 6.0) self.assertAlmostEqual(stds.victor_purpura_dist( [self.st07, self.st11], self.q5, algorithm='intuitive')[0, 1], 2.0, 5) self.assertEqual(stds.victor_purpura_dist( [self.st07, self.st11], self.q6, algorithm='intuitive')[0, 1], 2.0) # Tests on unordered spiketrains self.assertEqual(stds.victor_purpura_dist( [self.st11, self.st13], self.q4, algorithm='intuitive')[0, 1], stds.victor_purpura_dist( [self.st12, self.st13], self.q4, algorithm='intuitive')[0, 1]) self.assertNotEqual(stds.victor_purpura_dist( [self.st11, self.st13], self.q4, sort=False, algorithm='intuitive')[0, 1], stds.victor_purpura_dist( [self.st12, self.st13], self.q4, sort=False, algorithm='intuitive')[0, 1]) # Tests on metric properties with random spiketrains # (explicit calculation of second metric axiom in particular case, # because from dist_matrix it is trivial) dist_matrix = stds.victor_purpura_dist( [self.st21, self.st22, self.st23], self.q3, algorithm='intuitive') for i in range(3): for j in range(3): self.assertGreaterEqual(dist_matrix[i, j], 0) if dist_matrix[i, j] == 0: assert_array_equal(self.rd_st_list[i], self.rd_st_list[j]) assert_array_equal(stds.victor_purpura_dist( [self.st21, self.st22], self.q3, algorithm='intuitive'), stds.victor_purpura_dist( [self.st22, self.st21], self.q3, algorithm='intuitive')) self.assertLessEqual(dist_matrix[0, 1], dist_matrix[0, 2] + dist_matrix[1, 2]) self.assertLessEqual(dist_matrix[0, 2], dist_matrix[1, 2] + dist_matrix[0, 1]) self.assertLessEqual(dist_matrix[1, 2], dist_matrix[0, 1] + dist_matrix[0, 2]) # Tests on proper unit conversion self.assertAlmostEqual(stds.victor_purpura_dist( [self.st14, self.st16], self.q3, algorithm='intuitive')[0, 1], stds.victor_purpura_dist( [self.st15, self.st16], self.q3, algorithm='intuitive')[0, 1]) self.assertAlmostEqual(stds.victor_purpura_dist( [self.st16, self.st14], self.q3, algorithm='intuitive')[0, 1], stds.victor_purpura_dist( [self.st16, self.st15], self.q3, algorithm='intuitive')[0, 1]) self.assertEqual(stds.victor_purpura_dist( [self.st01, self.st05], self.q3, algorithm='intuitive')[0, 1], stds.victor_purpura_dist( [self.st01, self.st05], self.q7, algorithm='intuitive')[0, 1]) # Tests on algorithmic behaviour for equal spike times self.assertEqual(stds.victor_purpura_dist( [self.st31, self.st34], self.q3, algorithm='intuitive')[0, 1], 0.8 + 1.0) self.assertEqual(stds.victor_purpura_dist( [self.st31, self.st34], self.q3, algorithm='intuitive')[0, 1], stds.victor_purpura_dist( [self.st32, self.st33], self.q3, algorithm='intuitive')[0, 1]) self.assertEqual(stds.victor_purpura_dist( [self.st31, self.st33], self.q3, algorithm='intuitive')[0, 1] * 2.0, stds.victor_purpura_dist( [self.st32, self.st34], self.q3, algorithm='intuitive')[0, 1]) # Tests on spike train list lengthes smaller than 2 self.assertEqual(stds.victor_purpura_dist( [self.st21], self.q3, algorithm='intuitive')[0, 0], 0) self.assertEqual(len(stds.victor_purpura_dist( [], self.q3, algorithm='intuitive')), 0)
[docs] def test_victor_purpura_algorithm_comparison(self): assert_array_almost_equal( stds.victor_purpura_dist([self.st21, self.st22, self.st23], self.q3), stds.victor_purpura_dist([self.st21, self.st22, self.st23], self.q3, algorithm='intuitive'))
[docs] def test_van_rossum_distance(self): # Tests of distances of simplest spike trains self.assertEqual(stds.van_rossum_dist( [self.st00, self.st00], self.tau2)[0, 1], 0.0) self.assertEqual(stds.van_rossum_dist( [self.st00, self.st01], self.tau2)[0, 1], 1.0) self.assertEqual(stds.van_rossum_dist( [self.st01, self.st00], self.tau2)[0, 1], 1.0) self.assertEqual(stds.van_rossum_dist( [self.st01, self.st01], self.tau2)[0, 1], 0.0) # Tests of distances under elementary spike operations self.assertAlmostEqual(stds.van_rossum_dist( [self.st01, self.st02], self.tau2)[0, 1], float(np.sqrt(2*(1.0-np.exp(-np.absolute( ((self.st01[0]-self.st02[0]) / self.tau2).simplified)))))) self.assertAlmostEqual(stds.van_rossum_dist( [self.st01, self.st05], self.tau2)[0, 1], float(np.sqrt(2*(1.0-np.exp(-np.absolute( ((self.st01[0]-self.st05[0]) / self.tau2).simplified)))))) self.assertAlmostEqual(stds.van_rossum_dist( [self.st01, self.st05], self.tau2)[0, 1], np.sqrt(2.0), 1) self.assertAlmostEqual(stds.van_rossum_dist( [self.st01, self.st06], self.tau2)[0, 1], np.sqrt(2.0), 20) self.assertAlmostEqual(stds.van_rossum_dist( [self.st00, self.st07], self.tau1)[0, 1], np.sqrt(0 + 2)) self.assertAlmostEqual(stds.van_rossum_dist( [self.st07, self.st08], self.tau4)[0, 1], float(np.sqrt(2*(1.0-np.exp(-np.absolute( ((self.st07[0]-self.st08[-1]) / self.tau4).simplified)))))) f_minus_g_squared = ( (self.t > self.st08[0]) * np.exp( -((self.t-self.st08[0])/self.tau3).simplified) + (self.t > self.st08[1]) * np.exp( -((self.t-self.st08[1])/self.tau3).simplified) - (self.t > self.st09[0]) * np.exp( -((self.t-self.st09[0])/self.tau3).simplified))**2 distance = np.sqrt(2.0 * spint.cumtrapz( y=f_minus_g_squared, x=self.t.magnitude)[-1] / self.tau3.rescale(self.t.units).magnitude) self.assertAlmostEqual(stds.van_rossum_dist( [self.st08, self.st09], self.tau3)[0, 1], distance, 5) self.assertAlmostEqual(stds.van_rossum_dist( [self.st11, self.st14], self.tau2)[0, 1], 1) # Tests on timescales self.assertAlmostEqual( stds.van_rossum_dist([self.st11, self.st14], self.tau1)[0, 1], stds.van_rossum_dist([self.st11, self.st14], self.tau5)[0, 1]) self.assertAlmostEqual( stds.van_rossum_dist([self.st07, self.st11], self.tau0)[0, 1], np.sqrt(len(self.st07) + len(self.st11))) self.assertAlmostEqual( stds.van_rossum_dist([self.st07, self.st14], self.tau0)[0, 1], np.sqrt(len(self.st07) + len(self.st14))) self.assertAlmostEqual( stds.van_rossum_dist([self.st07, self.st11], self.tau1)[0, 1], np.sqrt(len(self.st07) + len(self.st11))) self.assertAlmostEqual( stds.van_rossum_dist([self.st07, self.st14], self.tau1)[0, 1], np.sqrt(len(self.st07) + len(self.st14))) self.assertAlmostEqual( stds.van_rossum_dist([self.st07, self.st11], self.tau5)[0, 1], np.absolute(len(self.st07) - len(self.st11))) self.assertAlmostEqual( stds.van_rossum_dist([self.st07, self.st14], self.tau5)[0, 1], np.absolute(len(self.st07) - len(self.st14))) self.assertAlmostEqual( stds.van_rossum_dist([self.st07, self.st11], self.tau6)[0, 1], np.absolute(len(self.st07) - len(self.st11))) self.assertAlmostEqual( stds.van_rossum_dist([self.st07, self.st14], self.tau6)[0, 1], np.absolute(len(self.st07) - len(self.st14))) # Tests on unordered spiketrains self.assertEqual( stds.van_rossum_dist([self.st11, self.st13], self.tau4)[0, 1], stds.van_rossum_dist([self.st12, self.st13], self.tau4)[0, 1]) self.assertNotEqual( stds.van_rossum_dist([self.st11, self.st13], self.tau4, sort=False)[0, 1], stds.van_rossum_dist([self.st12, self.st13], self.tau4, sort=False)[0, 1]) # Tests on metric properties with random spiketrains # (explicit calculation of second metric axiom in particular case, # because from dist_matrix it is trivial) dist_matrix = stds.van_rossum_dist( [self.st21, self.st22, self.st23], self.tau3) for i in range(3): for j in range(3): self.assertGreaterEqual(dist_matrix[i, j], 0) if dist_matrix[i, j] == 0: assert_array_equal(self.rd_st_list[i], self.rd_st_list[j]) assert_array_equal( stds.van_rossum_dist([self.st21, self.st22], self.tau3), stds.van_rossum_dist([self.st22, self.st21], self.tau3)) self.assertLessEqual(dist_matrix[0, 1], dist_matrix[0, 2] + dist_matrix[1, 2]) self.assertLessEqual(dist_matrix[0, 2], dist_matrix[1, 2] + dist_matrix[0, 1]) self.assertLessEqual(dist_matrix[1, 2], dist_matrix[0, 1] + dist_matrix[0, 2]) # Tests on proper unit conversion self.assertAlmostEqual( stds.van_rossum_dist([self.st14, self.st16], self.tau3)[0, 1], stds.van_rossum_dist([self.st15, self.st16], self.tau3)[0, 1]) self.assertAlmostEqual( stds.van_rossum_dist([self.st16, self.st14], self.tau3)[0, 1], stds.van_rossum_dist([self.st16, self.st15], self.tau3)[0, 1]) self.assertEqual( stds.van_rossum_dist([self.st01, self.st05], self.tau3)[0, 1], stds.van_rossum_dist([self.st01, self.st05], self.tau7)[0, 1]) # Tests on algorithmic behaviour for equal spike times f_minus_g_squared = ( (self.t > self.st31[0]) * np.exp( -((self.t-self.st31[0])/self.tau3).simplified) - (self.t > self.st34[0]) * np.exp( -((self.t-self.st34[0])/self.tau3).simplified) - (self.t > self.st34[1]) * np.exp( -((self.t-self.st34[1])/self.tau3).simplified))**2 distance = np.sqrt(2.0 * spint.cumtrapz( y=f_minus_g_squared, x=self.t.magnitude)[-1] / self.tau3.rescale(self.t.units).magnitude) self.assertAlmostEqual(stds.van_rossum_dist([self.st31, self.st34], self.tau3)[0, 1], distance, 5) self.assertEqual(stds.van_rossum_dist([self.st31, self.st34], self.tau3)[0, 1], stds.van_rossum_dist([self.st32, self.st33], self.tau3)[0, 1]) self.assertEqual(stds.van_rossum_dist([self.st31, self.st33], self.tau3)[0, 1] * 2.0, stds.van_rossum_dist([self.st32, self.st34], self.tau3)[0, 1]) # Tests on spike train list lengthes smaller than 2 self.assertEqual(stds.van_rossum_dist([self.st21], self.tau3)[0, 0], 0) self.assertEqual(len(stds.van_rossum_dist([], self.tau3)), 0)
if __name__ == '__main__': unittest.main()