def test_victor_purpura_algorithm_comparison(self): assert_array_almost_equal( stds.victor_purpura_distance([self.st21, self.st22, self.st23], self.q3), stds.victor_purpura_distance([self.st21, self.st22, self.st23], self.q3, algorithm='intuitive'))
def test_victor_purpura_matlab_comparison_int(self): repo_path =\ r"unittest/spike_train_dissimilarity/victor_purpura_distance/data" files_to_download = [ ("times_int.npy", "aa1411c04da3f58d8b8913ae2f935057"), ("matlab_results_int.npy", "7edd32e50edde12dc1ef4aa5f57f70fb") ] for filename, checksum in files_to_download: download_datasets(repo_path=f"{repo_path}/{filename}", checksum=checksum) times_int = np.load(ELEPHANT_TMP_DIR / 'times_int.npy') mat_res_int = np.load(ELEPHANT_TMP_DIR / 'matlab_results_int.npy') r_int = SpikeTrain(times_int[0], units='ms', t_start=0, t_stop=1000 * ms) s_int = SpikeTrain(times_int[1], units='ms', t_start=0, t_stop=1000 * ms) t_int = SpikeTrain(times_int[2], units='ms', t_start=0, t_stop=1000 * ms) vic_pur_result_int = stds.victor_purpura_distance( [r_int, s_int, t_int], cost_factor=1.0 / ms, kernel=None, sort=True, algorithm='intuitive') assert_array_equal(vic_pur_result_int, mat_res_int)
def test_victor_purpura_matlab_comparison_float(self): repo_path =\ r"unittest/spike_train_dissimilarity/victor_purpura_distance/data" files_to_download = [ ("times_float.npy", "ed1ff4d2c0eeed4a2b50a456803656be"), ("matlab_results_float.npy", "a17f049e7ad0ddf7ca812e86fdb92646") ] for filename, checksum in files_to_download: download_datasets(repo_path=f"{repo_path}/{filename}", checksum=checksum) times_float = np.load(ELEPHANT_TMP_DIR / 'times_float.npy') mat_res_float = np.load(ELEPHANT_TMP_DIR / 'matlab_results_float.npy') r_float = SpikeTrain(times_float[0], units='ms', t_start=0, t_stop=1000 * ms) s_float = SpikeTrain(times_float[1], units='ms', t_start=0, t_stop=1000 * ms) t_float = SpikeTrain(times_float[2], units='ms', t_start=0, t_stop=1000 * ms) vic_pur_result_float = stds.victor_purpura_distance( [r_float, s_float, t_float], cost_factor=1.0 / ms, kernel=None, sort=True, algorithm='intuitive') assert_array_almost_equal(vic_pur_result_float, mat_res_float)
def test_wrong_input(self): self.assertRaises(TypeError, stds.victor_purpura_distance, [self.array1, self.array2], self.q3) self.assertRaises(TypeError, stds.victor_purpura_distance, [self.qarray1, self.qarray2], self.q3) self.assertRaises(TypeError, stds.victor_purpura_distance, [self.qarray1, self.qarray2], 5.0 * ms) self.assertRaises(TypeError, stds.victor_purpura_distance, [self.array1, self.array2], self.q3, algorithm='intuitive') self.assertRaises(TypeError, stds.victor_purpura_distance, [self.qarray1, self.qarray2], self.q3, algorithm='intuitive') self.assertRaises(TypeError, stds.victor_purpura_distance, [self.qarray1, self.qarray2], 5.0 * ms, algorithm='intuitive') self.assertRaises(TypeError, stds.van_rossum_distance, [self.array1, self.array2], self.tau3) self.assertRaises(TypeError, stds.van_rossum_distance, [self.qarray1, self.qarray2], self.tau3) self.assertRaises(TypeError, stds.van_rossum_distance, [self.qarray1, self.qarray2], 5.0 * Hz) self.assertRaises(TypeError, stds.victor_purpura_distance, [self.st11, self.st13], self.tau2) self.assertRaises(TypeError, stds.victor_purpura_distance, [self.st11, self.st13], 5.0) self.assertRaises(TypeError, stds.victor_purpura_distance, [self.st11, self.st13], self.tau2, algorithm='intuitive') self.assertRaises(TypeError, stds.victor_purpura_distance, [self.st11, self.st13], 5.0, algorithm='intuitive') self.assertRaises(TypeError, stds.van_rossum_distance, [self.st11, self.st13], self.q4) self.assertRaises(TypeError, stds.van_rossum_distance, [self.st11, self.st13], 5.0) self.assertRaises(NotImplementedError, stds.victor_purpura_distance, [self.st01, self.st02], self.q3, kernel=kernels.Kernel(2.0 / self.q3)) self.assertRaises(NotImplementedError, stds.victor_purpura_distance, [self.st01, self.st02], self.q3, kernel=kernels.SymmetricKernel(2.0 / self.q3)) self.assertEqual( stds.victor_purpura_distance( [self.st01, self.st02], self.q1, kernel=kernels.TriangularKernel( 2.0 / (np.sqrt(6.0) * self.q2)))[0, 1], stds.victor_purpura_distance( [self.st01, self.st02], self.q3, kernel=kernels.TriangularKernel( 2.0 / (np.sqrt(6.0) * self.q2)))[0, 1]) self.assertEqual( stds.victor_purpura_distance( [self.st01, self.st02], kernel=kernels.TriangularKernel( 2.0 / (np.sqrt(6.0) * self.q2)))[0, 1], 1.0) self.assertNotEqual( stds.victor_purpura_distance( [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_distance, [self.st11, self.st13], self.q2, algorithm='slow')
def test_victor_purpura_distance_intuitive(self): # Tests of distances of simplest spike trains self.assertEqual( stds.victor_purpura_distance([self.st00, self.st00], self.q2, algorithm='intuitive')[0, 1], 0.0) self.assertEqual( stds.victor_purpura_distance([self.st00, self.st01], self.q2, algorithm='intuitive')[0, 1], 1.0) self.assertEqual( stds.victor_purpura_distance([self.st01, self.st00], self.q2, algorithm='intuitive')[0, 1], 1.0) self.assertEqual( stds.victor_purpura_distance([self.st01, self.st01], self.q2, algorithm='intuitive')[0, 1], 0.0) # Tests of distances under elementary spike operations self.assertEqual( stds.victor_purpura_distance([self.st01, self.st02], self.q2, algorithm='intuitive')[0, 1], 1.0) self.assertEqual( stds.victor_purpura_distance([self.st01, self.st03], self.q2, algorithm='intuitive')[0, 1], 1.9) self.assertEqual( stds.victor_purpura_distance([self.st01, self.st04], self.q2, algorithm='intuitive')[0, 1], 2.0) self.assertEqual( stds.victor_purpura_distance([self.st01, self.st05], self.q2, algorithm='intuitive')[0, 1], 2.0) self.assertEqual( stds.victor_purpura_distance([self.st00, self.st07], self.q2, algorithm='intuitive')[0, 1], 2.0) self.assertAlmostEqual( stds.victor_purpura_distance([self.st07, self.st08], self.q4, algorithm='intuitive')[0, 1], 0.4) self.assertAlmostEqual( stds.victor_purpura_distance([self.st07, self.st10], self.q3, algorithm='intuitive')[0, 1], 2.6) self.assertEqual( stds.victor_purpura_distance([self.st11, self.st14], self.q2, algorithm='intuitive')[0, 1], 1) # Tests on timescales self.assertEqual( stds.victor_purpura_distance([self.st11, self.st14], self.q1, algorithm='intuitive')[0, 1], stds.victor_purpura_distance([self.st11, self.st14], self.q5, algorithm='intuitive')[0, 1]) self.assertEqual( stds.victor_purpura_distance([self.st07, self.st11], self.q0, algorithm='intuitive')[0, 1], 6.0) self.assertEqual( stds.victor_purpura_distance([self.st07, self.st11], self.q1, algorithm='intuitive')[0, 1], 6.0) self.assertAlmostEqual( stds.victor_purpura_distance([self.st07, self.st11], self.q5, algorithm='intuitive')[0, 1], 2.0, 5) self.assertEqual( stds.victor_purpura_distance([self.st07, self.st11], self.q6, algorithm='intuitive')[0, 1], 2.0) # Tests on unordered spiketrains self.assertEqual( stds.victor_purpura_distance([self.st11, self.st13], self.q4, algorithm='intuitive')[0, 1], stds.victor_purpura_distance([self.st12, self.st13], self.q4, algorithm='intuitive')[0, 1]) self.assertNotEqual( stds.victor_purpura_distance([self.st11, self.st13], self.q4, sort=False, algorithm='intuitive')[0, 1], stds.victor_purpura_distance([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_distance( [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_distance([self.st21, self.st22], self.q3, algorithm='intuitive'), stds.victor_purpura_distance([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_distance([self.st14, self.st16], self.q3, algorithm='intuitive')[0, 1], stds.victor_purpura_distance([self.st15, self.st16], self.q3, algorithm='intuitive')[0, 1]) self.assertAlmostEqual( stds.victor_purpura_distance([self.st16, self.st14], self.q3, algorithm='intuitive')[0, 1], stds.victor_purpura_distance([self.st16, self.st15], self.q3, algorithm='intuitive')[0, 1]) self.assertEqual( stds.victor_purpura_distance([self.st01, self.st05], self.q3, algorithm='intuitive')[0, 1], stds.victor_purpura_distance([self.st01, self.st05], self.q7, algorithm='intuitive')[0, 1]) # Tests on algorithmic behaviour for equal spike times self.assertEqual( stds.victor_purpura_distance([self.st31, self.st34], self.q3, algorithm='intuitive')[0, 1], 0.8 + 1.0) self.assertEqual( stds.victor_purpura_distance([self.st31, self.st34], self.q3, algorithm='intuitive')[0, 1], stds.victor_purpura_distance([self.st32, self.st33], self.q3, algorithm='intuitive')[0, 1]) self.assertEqual( stds.victor_purpura_distance( [self.st31, self.st33], self.q3, algorithm='intuitive')[0, 1] * 2.0, stds.victor_purpura_distance([self.st32, self.st34], self.q3, algorithm='intuitive')[0, 1]) # Tests on spike train list lengthes smaller than 2 self.assertEqual( stds.victor_purpura_distance([self.st21], self.q3, algorithm='intuitive')[0, 0], 0) self.assertEqual( len( stds.victor_purpura_distance([], self.q3, algorithm='intuitive')), 0)