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)