multielectrode_grasp/code/elephant/elephant/test/test_spike_train_correlation.py

# -*- coding: utf-8 -*-
"""
Unit tests for the spike_train_correlation module.

:copyright: Copyright 2015-2016 by the Elephant team, see AUTHORS.txt.
:license: Modified BSD, see LICENSE.txt for details.
"""

import unittest

import numpy as np
from numpy.testing.utils import assert_array_equal, assert_array_almost_equal
import quantities as pq
import neo
import elephant.conversion as conv
import elephant.spike_train_correlation as sc


class covariance_TestCase(unittest.TestCase):

    def setUp(self):
        # These two arrays must be such that they do not have coincidences
        # spanning across two neighbor bins assuming ms bins [0,1),[1,2),...
        self.test_array_1d_0 = [
            1.3, 7.56, 15.87, 28.23, 30.9, 34.2, 38.2, 43.2]
        self.test_array_1d_1 = [
            1.02, 2.71, 18.82, 28.46, 28.79, 43.6]

        # Build spike trains
        self.st_0 = neo.SpikeTrain(
            self.test_array_1d_0, units='ms', t_stop=50.)
        self.st_1 = neo.SpikeTrain(
            self.test_array_1d_1, units='ms', t_stop=50.)

        # And binned counterparts
        self.binned_st = conv.BinnedSpikeTrain(
            [self.st_0, self.st_1], t_start=0 * pq.ms, t_stop=50. * pq.ms,
            binsize=1 * pq.ms)

    def test_covariance_binned(self):
        '''
        Test covariance between two binned spike trains.
        '''

        # Calculate clipped and unclipped
        res_clipped = sc.covariance(
            self.binned_st, binary=True)
        res_unclipped = sc.covariance(
            self.binned_st, binary=False)

        # Check dimensions
        self.assertEqual(len(res_clipped), 2)
        self.assertEqual(len(res_unclipped), 2)

        # Check result unclipped against result calculated from scratch for
        # the off-diagonal element
        mat = self.binned_st.to_array()
        mean_0 = np.mean(mat[0])
        mean_1 = np.mean(mat[1])
        target_from_scratch = \
            np.dot(mat[0] - mean_0, mat[1] - mean_1) / (len(mat[0]) - 1)

        # Check result unclipped against result calculated by numpy.corrcoef
        target_numpy = np.cov(mat)

        self.assertAlmostEqual(target_from_scratch, target_numpy[0][1])
        self.assertAlmostEqual(res_unclipped[0][1], target_from_scratch)
        self.assertAlmostEqual(res_unclipped[1][0], target_from_scratch)

        # Check result clipped against result calculated from scratch for
        # the off-diagonal elemant
        mat = self.binned_st.to_bool_array()
        mean_0 = np.mean(mat[0])
        mean_1 = np.mean(mat[1])
        target_from_scratch = \
            np.dot(mat[0] - mean_0, mat[1] - mean_1) / (len(mat[0]) - 1)

        # Check result unclipped against result calculated by numpy.corrcoef
        target_numpy = np.cov(mat)

        self.assertAlmostEqual(target_from_scratch, target_numpy[0][1])
        self.assertAlmostEqual(res_clipped[0][1], target_from_scratch)
        self.assertAlmostEqual(res_clipped[1][0], target_from_scratch)

    def test_covariance_binned_same_spiketrains(self):
        '''
        Test if the covariation between two identical binned spike
        trains evaluates to the expected 2x2 matrix.
        '''
        # Calculate correlation
        binned_st = conv.BinnedSpikeTrain(
            [self.st_0, self.st_0], t_start=0 * pq.ms, t_stop=50. * pq.ms,
            binsize=1 * pq.ms)
        target = sc.covariance(binned_st)

        # Check dimensions
        self.assertEqual(len(target), 2)
        # Check result
        assert_array_equal(target[0][0], target[1][1])

    def test_covariance_binned_short_input(self):
        '''
        Test if input list of only one binned spike train yields correct result
        that matches numpy.cov (covariance with itself)
        '''
        # Calculate correlation
        binned_st = conv.BinnedSpikeTrain(
            self.st_0, t_start=0 * pq.ms, t_stop=50. * pq.ms,
            binsize=1 * pq.ms)
        target = sc.covariance(binned_st)

        # Check result unclipped against result calculated by numpy.corrcoef
        mat = binned_st.to_bool_array()
        target_numpy = np.cov(mat)

        # Check result and dimensionality of result
        self.assertEqual(target.ndim, target_numpy.ndim)
        self.assertAlmostEqual(target, target_numpy)


class corrcoeff_TestCase(unittest.TestCase):

    def setUp(self):
        # These two arrays must be such that they do not have coincidences
        # spanning across two neighbor bins assuming ms bins [0,1),[1,2),...
        self.test_array_1d_0 = [
            1.3, 7.56, 15.87, 28.23, 30.9, 34.2, 38.2, 43.2]
        self.test_array_1d_1 = [
            1.02, 2.71, 18.82, 28.46, 28.79, 43.6]

        # Build spike trains
        self.st_0 = neo.SpikeTrain(
            self.test_array_1d_0, units='ms', t_stop=50.)
        self.st_1 = neo.SpikeTrain(
            self.test_array_1d_1, units='ms', t_stop=50.)

        # And binned counterparts
        self.binned_st = conv.BinnedSpikeTrain(
            [self.st_0, self.st_1], t_start=0 * pq.ms, t_stop=50. * pq.ms,
            binsize=1 * pq.ms)

    def test_corrcoef_binned(self):
        '''
        Test the correlation coefficient between two binned spike trains.
        '''

        # Calculate clipped and unclipped
        res_clipped = sc.corrcoef(
            self.binned_st, binary=True)
        res_unclipped = sc.corrcoef(
            self.binned_st, binary=False)

        # Check dimensions
        self.assertEqual(len(res_clipped), 2)
        self.assertEqual(len(res_unclipped), 2)

        # Check result unclipped against result calculated from scratch for
        # the off-diagonal element
        mat = self.binned_st.to_array()
        mean_0 = np.mean(mat[0])
        mean_1 = np.mean(mat[1])
        target_from_scratch = \
            np.dot(mat[0] - mean_0, mat[1] - mean_1) / \
            np.sqrt(
                np.dot(mat[0] - mean_0, mat[0] - mean_0) *
                np.dot(mat[1] - mean_1, mat[1] - mean_1))

        # Check result unclipped against result calculated by numpy.corrcoef
        target_numpy = np.corrcoef(mat)

        self.assertAlmostEqual(target_from_scratch, target_numpy[0][1])
        self.assertAlmostEqual(res_unclipped[0][1], target_from_scratch)
        self.assertAlmostEqual(res_unclipped[1][0], target_from_scratch)

        # Check result clipped against result calculated from scratch for
        # the off-diagonal elemant
        mat = self.binned_st.to_bool_array()
        mean_0 = np.mean(mat[0])
        mean_1 = np.mean(mat[1])
        target_from_scratch = \
            np.dot(mat[0] - mean_0, mat[1] - mean_1) / \
            np.sqrt(
                np.dot(mat[0] - mean_0, mat[0] - mean_0) *
                np.dot(mat[1] - mean_1, mat[1] - mean_1))

        # Check result unclipped against result calculated by numpy.corrcoef
        target_numpy = np.corrcoef(mat)

        self.assertAlmostEqual(target_from_scratch, target_numpy[0][1])
        self.assertAlmostEqual(res_clipped[0][1], target_from_scratch)
        self.assertAlmostEqual(res_clipped[1][0], target_from_scratch)

    def test_corrcoef_binned_same_spiketrains(self):
        '''
        Test if the correlation coefficient between two identical binned spike
        trains evaluates to a 2x2 matrix of ones.
        '''
        # Calculate correlation
        binned_st = conv.BinnedSpikeTrain(
            [self.st_0, self.st_0], t_start=0 * pq.ms, t_stop=50. * pq.ms,
            binsize=1 * pq.ms)
        target = sc.corrcoef(binned_st)

        # Check dimensions
        self.assertEqual(len(target), 2)
        # Check result
        assert_array_equal(target, 1.)

    def test_corrcoef_binned_short_input(self):
        '''
        Test if input list of one binned spike train yields 1.0.
        '''
        # Calculate correlation
        binned_st = conv.BinnedSpikeTrain(
            self.st_0, t_start=0 * pq.ms, t_stop=50. * pq.ms,
            binsize=1 * pq.ms)
        target = sc.corrcoef(binned_st)

        # Check result and dimensionality of result
        self.assertEqual(target.ndim, 0)
        self.assertEqual(target, 1.)


class cross_correlation_histogram_TestCase(unittest.TestCase):

    def setUp(self):
        # These two arrays must be such that they do not have coincidences
        # spanning across two neighbor bins assuming ms bins [0,1),[1,2),...
        self.test_array_1d_1 = [
            1.3, 7.56, 15.87, 28.23, 30.9, 34.2, 38.2, 43.2]
        self.test_array_1d_2 = [
            1.02, 2.71, 18.82, 28.46, 28.79, 43.6]

        # Build spike trains
        self.st_1 = neo.SpikeTrain(
            self.test_array_1d_1, units='ms', t_stop=50.)
        self.st_2 = neo.SpikeTrain(
            self.test_array_1d_2, units='ms', t_stop=50.)

        # And binned counterparts
        self.binned_st1 = conv.BinnedSpikeTrain(
            [self.st_1], t_start=0 * pq.ms, t_stop=50. * pq.ms,
            binsize=1 * pq.ms)
        self.binned_st2 = conv.BinnedSpikeTrain(
            [self.st_2], t_start=0 * pq.ms, t_stop=50. * pq.ms,
            binsize=1 * pq.ms)
        self.binned_sts = conv.BinnedSpikeTrain(
            [self.st_1, self.st_2], t_start=0 * pq.ms, t_stop=50. * pq.ms,
            binsize=1 * pq.ms)

        # Binned sts to check errors raising
        self.st_check_binsize = conv.BinnedSpikeTrain(
            [self.st_1], t_start=0 * pq.ms, t_stop=50. * pq.ms,
            binsize=5 * pq.ms)
        self.st_check_t_start = conv.BinnedSpikeTrain(
            [self.st_1], t_start=1 * pq.ms, t_stop=50. * pq.ms,
            binsize=1 * pq.ms)
        self.st_check_t_stop = conv.BinnedSpikeTrain(
            [self.st_1], t_start=0 * pq.ms, t_stop=40. * pq.ms,
            binsize=1 * pq.ms)
        self.st_check_dimension = conv.BinnedSpikeTrain(
            [self.st_1, self.st_2], t_start=0 * pq.ms, t_stop=50. * pq.ms,
            binsize=1 * pq.ms)

    def test_cross_correlation_histogram(self):
        '''
        Test generic result of a cross-correlation histogram between two binned
        spike trains.
        '''
        # Calculate CCH using Elephant (normal and binary version) with
        # mode equal to 'full' (whole spike trains are correlated)
        cch_clipped, bin_ids_clipped = sc.cross_correlation_histogram(
            self.binned_st1, self.binned_st2, window='full',
            binary=True)
        cch_unclipped, bin_ids_unclipped = sc.cross_correlation_histogram(
            self.binned_st1, self.binned_st2, window='full', binary=False)

        cch_clipped_mem, bin_ids_clipped_mem = sc.cross_correlation_histogram(
            self.binned_st1, self.binned_st2, window='full',
            binary=True, method='memory')
        cch_unclipped_mem, bin_ids_unclipped_mem = \
            sc.cross_correlation_histogram(
                self.binned_st1, self.binned_st2, window='full',
                binary=False, method='memory')
        # Check consistency two methods
        assert_array_equal(
            np.squeeze(cch_clipped.magnitude), np.squeeze(
                cch_clipped_mem.magnitude))
        assert_array_equal(
            np.squeeze(cch_clipped.times), np.squeeze(
                cch_clipped_mem.times))
        assert_array_equal(
            np.squeeze(cch_unclipped.magnitude), np.squeeze(
                cch_unclipped_mem.magnitude))
        assert_array_equal(
            np.squeeze(cch_unclipped.times), np.squeeze(
                cch_unclipped_mem.times))
        assert_array_almost_equal(bin_ids_clipped, bin_ids_clipped_mem)
        assert_array_almost_equal(bin_ids_unclipped, bin_ids_unclipped_mem)

        # Check normal correlation Note: Use numpy correlate to verify result.
        # Note: numpy conventions for input array 1 and input array 2 are
        # swapped compared to Elephant!
        mat1 = self.binned_st1.to_array()[0]
        mat2 = self.binned_st2.to_array()[0]
        target_numpy = np.correlate(mat2, mat1, mode='full')
        assert_array_equal(
            target_numpy, np.squeeze(cch_unclipped.magnitude))

        # Check cross correlation function for several displacements tau
        # Note: Use Elephant corrcoeff to verify result
        tau = [-25.0, 0.0, 13.0]  # in ms
        for t in tau:
            # adjust t_start, t_stop to shift by tau
            t0 = np.min([self.st_1.t_start + t * pq.ms, self.st_2.t_start])
            t1 = np.max([self.st_1.t_stop + t * pq.ms, self.st_2.t_stop])
            st1 = neo.SpikeTrain(self.st_1.magnitude + t, units='ms',
                                 t_start=t0 * pq.ms, t_stop=t1 * pq.ms)
            st2 = neo.SpikeTrain(self.st_2.magnitude, units='ms',
                                 t_start=t0 * pq.ms, t_stop=t1 * pq.ms)
            binned_sts = conv.BinnedSpikeTrain([st1, st2],
                                               binsize=1 * pq.ms,
                                               t_start=t0 * pq.ms,
                                               t_stop=t1 * pq.ms)
            # caluclate corrcoef
            corrcoef = sc.corrcoef(binned_sts)[1, 0]

            # expand t_stop to have two spike trains with same length as st1,
            # st2
            st1 = neo.SpikeTrain(self.st_1.magnitude, units='ms',
                                 t_start=self.st_1.t_start,
                                 t_stop=self.st_1.t_stop + np.abs(t) * pq.ms)
            st2 = neo.SpikeTrain(self.st_2.magnitude, units='ms',
                                 t_start=self.st_2.t_start,
                                 t_stop=self.st_2.t_stop + np.abs(t) * pq.ms)
            binned_st1 = conv.BinnedSpikeTrain(
                st1, t_start=0 * pq.ms, t_stop=(50 + np.abs(t)) * pq.ms,
                binsize=1 * pq.ms)
            binned_st2 = conv.BinnedSpikeTrain(
                st2, t_start=0 * pq.ms, t_stop=(50 + np.abs(t)) * pq.ms,
                binsize=1 * pq.ms)
            # calculate CCHcoef and take value at t=tau
            CCHcoef, _ = sc.cch(binned_st1, binned_st2,
                                cross_corr_coef=True)
            left_edge = - binned_st1.num_bins + 1
            tau_bin = int(t / float(binned_st1.binsize.magnitude))
            assert_array_equal(
                corrcoef, CCHcoef[tau_bin - left_edge].magnitude)

        # Check correlation using binary spike trains
        mat1 = np.array(self.binned_st1.to_bool_array()[0], dtype=int)
        mat2 = np.array(self.binned_st2.to_bool_array()[0], dtype=int)
        target_numpy = np.correlate(mat2, mat1, mode='full')
        assert_array_equal(
            target_numpy, np.squeeze(cch_clipped.magnitude))

        # Check the time axis and bin IDs of the resulting AnalogSignal
        assert_array_almost_equal(
            (bin_ids_clipped - 0.5) * self.binned_st1.binsize,
            cch_unclipped.times)
        assert_array_almost_equal(
            (bin_ids_clipped - 0.5) * self.binned_st1.binsize,
            cch_clipped.times)

        # Calculate CCH using Elephant (normal and binary version) with
        # mode equal to 'valid' (only completely overlapping intervals of the
        # spike trains are correlated)
        cch_clipped, bin_ids_clipped = sc.cross_correlation_histogram(
            self.binned_st1, self.binned_st2, window='valid',
            binary=True)
        cch_unclipped, bin_ids_unclipped = sc.cross_correlation_histogram(
            self.binned_st1, self.binned_st2, window='valid',
            binary=False)
        cch_clipped_mem, bin_ids_clipped_mem = sc.cross_correlation_histogram(
            self.binned_st1, self.binned_st2, window='valid',
            binary=True, method='memory')
        cch_unclipped_mem, bin_ids_unclipped_mem = \
            sc.cross_correlation_histogram(
                self.binned_st1, self.binned_st2, window='valid',
                binary=False, method='memory')

        # Check consistency two methods
        assert_array_equal(
            np.squeeze(cch_clipped.magnitude), np.squeeze(
                cch_clipped_mem.magnitude))
        assert_array_equal(
            np.squeeze(cch_clipped.times), np.squeeze(
                cch_clipped_mem.times))
        assert_array_equal(
            np.squeeze(cch_unclipped.magnitude), np.squeeze(
                cch_unclipped_mem.magnitude))
        assert_array_equal(
            np.squeeze(cch_unclipped.times), np.squeeze(
                cch_unclipped_mem.times))
        assert_array_equal(bin_ids_clipped, bin_ids_clipped_mem)
        assert_array_equal(bin_ids_unclipped, bin_ids_unclipped_mem)

        # Check normal correlation Note: Use numpy correlate to verify result.
        # Note: numpy conventions for input array 1 and input array 2 are
        # swapped compared to Elephant!
        mat1 = self.binned_st1.to_array()[0]
        mat2 = self.binned_st2.to_array()[0]
        target_numpy = np.correlate(mat2, mat1, mode='valid')
        assert_array_equal(
            target_numpy, np.squeeze(cch_unclipped.magnitude))

        # Check correlation using binary spike trains
        mat1 = np.array(self.binned_st1.to_bool_array()[0], dtype=int)
        mat2 = np.array(self.binned_st2.to_bool_array()[0], dtype=int)
        target_numpy = np.correlate(mat2, mat1, mode='valid')
        assert_array_equal(
            target_numpy, np.squeeze(cch_clipped.magnitude))

        # Check the time axis and bin IDs of the resulting AnalogSignal
        assert_array_equal(
            (bin_ids_clipped - 0.5) * self.binned_st1.binsize,
            cch_unclipped.times)
        assert_array_equal(
            (bin_ids_clipped - 0.5) * self.binned_st1.binsize,
            cch_clipped.times)

        # Check for wrong window parameter setting
        self.assertRaises(
            KeyError, sc.cross_correlation_histogram, self.binned_st1,
            self.binned_st2, window='dsaij')
        self.assertRaises(
            KeyError, sc.cross_correlation_histogram, self.binned_st1,
            self.binned_st2, window='dsaij', method='memory')

    def test_raising_error_wrong_inputs(self):
        '''Check that an exception is thrown if the two spike trains are not
        fullfilling the requirement of the function'''
        # Check the binsizes are the same
        self.assertRaises(
            AssertionError,
            sc.cross_correlation_histogram, self.binned_st1,
            self.st_check_binsize)
        # Check different t_start and t_stop
        self.assertRaises(
            AssertionError, sc.cross_correlation_histogram,
            self.st_check_t_start, self.binned_st2)
        self.assertRaises(
            AssertionError, sc.cross_correlation_histogram,
            self.st_check_t_stop, self.binned_st2)
        # Check input are one dimensional
        self.assertRaises(
            AssertionError, sc.cross_correlation_histogram,
            self.st_check_dimension, self.binned_st2)
        self.assertRaises(
            AssertionError, sc.cross_correlation_histogram,
            self.binned_st2, self.st_check_dimension)

    def test_window(self):
        '''Test if the window parameter is correctly interpreted.'''
        cch_win, bin_ids = sc.cch(
            self.binned_st1, self.binned_st2, window=[-30, 30])
        cch_win_mem, bin_ids_mem = sc.cch(
            self.binned_st1, self.binned_st2, window=[-30, 30])

        assert_array_equal(bin_ids, np.arange(-30, 31, 1))
        assert_array_equal(
            (bin_ids - 0.5) * self.binned_st1.binsize, cch_win.times)

        assert_array_equal(bin_ids_mem, np.arange(-30, 31, 1))
        assert_array_equal(
            (bin_ids_mem - 0.5) * self.binned_st1.binsize, cch_win.times)

        assert_array_equal(cch_win, cch_win_mem)
        cch_unclipped, _ = sc.cross_correlation_histogram(
            self.binned_st1, self.binned_st2, window='full', binary=False)
        assert_array_equal(cch_win, cch_unclipped[19:80])

        _, bin_ids = sc.cch(
            self.binned_st1, self.binned_st2, window=[20, 30])
        _, bin_ids_mem = sc.cch(
            self.binned_st1, self.binned_st2, window=[20, 30], method='memory')

        assert_array_equal(bin_ids, np.arange(20, 31, 1))
        assert_array_equal(bin_ids_mem, np.arange(20, 31, 1))

        _, bin_ids = sc.cch(
            self.binned_st1, self.binned_st2, window=[-30, -20])

        _, bin_ids_mem = sc.cch(
            self.binned_st1, self.binned_st2, window=[-30, -20],
            method='memory')

        assert_array_equal(bin_ids, np.arange(-30, -19, 1))
        assert_array_equal(bin_ids_mem, np.arange(-30, -19, 1))

        # Check for wrong assignments to the window parameter
        # Test for window longer than the total length of the spike trains
        self.assertRaises(
            ValueError, sc.cross_correlation_histogram, self.binned_st1,
            self.binned_st2, window=[-60, 50])
        self.assertRaises(
            ValueError, sc.cross_correlation_histogram, self.binned_st1,
            self.binned_st2, window=[-50, 60])
        # Test for no integer or wrong string in input
        self.assertRaises(
            KeyError, sc.cross_correlation_histogram, self.binned_st1,
            self.binned_st2, window=[-25.5, 25.5])
        self.assertRaises(
            KeyError, sc.cross_correlation_histogram, self.binned_st1,
            self.binned_st2, window='test')

    def test_border_correction(self):
        '''Test if the border correction for bins at the edges is correctly
        performed'''
        cch_corrected, _ = sc.cross_correlation_histogram(
            self.binned_st1, self.binned_st2, window='full',
            border_correction=True, binary=False, kernel=None)
        cch_corrected_mem, _ = sc.cross_correlation_histogram(
            self.binned_st1, self.binned_st2, window='full',
            border_correction=True, binary=False, kernel=None, method='memory')
        cch, _ = sc.cross_correlation_histogram(
            self.binned_st1, self.binned_st2, window='full',
            border_correction=False, binary=False, kernel=None)
        cch_mem, _ = sc.cross_correlation_histogram(
            self.binned_st1, self.binned_st2, window='full',
            border_correction=False, binary=False, kernel=None,
            method='memory')

        self.assertNotEqual(cch.all(), cch_corrected.all())
        self.assertNotEqual(cch_mem.all(), cch_corrected_mem.all())

    def test_kernel(self):
        '''Test if the smoothing kernel is correctly defined, and wheter it is
        applied properly.'''
        smoothed_cch, _ = sc.cross_correlation_histogram(
            self.binned_st1, self.binned_st2, kernel=np.ones(3))
        smoothed_cch_mem, _ = sc.cross_correlation_histogram(
            self.binned_st1, self.binned_st2, kernel=np.ones(3),
            method='memory')

        cch, _ = sc.cross_correlation_histogram(
            self.binned_st1, self.binned_st2, kernel=None)
        cch_mem, _ = sc.cross_correlation_histogram(
            self.binned_st1, self.binned_st2, kernel=None, method='memory')

        self.assertNotEqual(smoothed_cch.all, cch.all)
        self.assertNotEqual(smoothed_cch_mem.all, cch_mem.all)

        self.assertRaises(
            ValueError, sc.cch, self.binned_st1, self.binned_st2,
            kernel=np.ones(100))
        self.assertRaises(
            ValueError, sc.cch, self.binned_st1, self.binned_st2,
            kernel=np.ones(100), method='memory')

        self.assertRaises(
            ValueError, sc.cch, self.binned_st1, self.binned_st2, kernel='BOX')
        self.assertRaises(
            ValueError, sc.cch, self.binned_st1, self.binned_st2, kernel='BOX',
            method='memory')

    def test_exist_alias(self):
        '''
        Test if alias cch still exists.
        '''
        self.assertEqual(sc.cross_correlation_histogram, sc.cch)


class SpikeTimeTilingCoefficientTestCase(unittest.TestCase):

    def setUp(self):
        # These two arrays must be such that they do not have coincidences
        # spanning across two neighbor bins assuming ms bins [0,1),[1,2),...
        self.test_array_1d_1 = [
            1.3, 7.56, 15.87, 28.23, 30.9, 34.2, 38.2, 43.2]
        self.test_array_1d_2 = [
            1.02, 2.71, 18.82, 28.46, 28.79, 43.6]

        # Build spike trains
        self.st_1 = neo.SpikeTrain(
            self.test_array_1d_1, units='ms', t_stop=50.)
        self.st_2 = neo.SpikeTrain(
            self.test_array_1d_2, units='ms', t_stop=50.)

    def test_sttc(self):
        # test for result
        target = 0.8748350567
        self.assertAlmostEqual(target, sc.sttc(self.st_1, self.st_2,
                                               0.005 * pq.s))
        # test no spiketrains
        self.assertTrue(np.isnan(sc.sttc([], [])))

        # test one spiketrain
        self.assertTrue(np.isnan(sc.sttc(self.st_1, [])))

        # test for one spike in a spiketrain
        st1 = neo.SpikeTrain([1], units='ms', t_stop=1.)
        st2 = neo.SpikeTrain([5], units='ms', t_stop=10.)
        self.assertEqual(sc.sttc(st1, st2), 1.0)
        self.assertTrue(bool(sc.sttc(st1, st2, 0.1 * pq.ms) < 0))

        # test for high value of dt
        self.assertEqual(sc.sttc(self.st_1, self.st_2, dt=5 * pq.s), 1.0)

    def test_exist_alias(self):
        # Test if alias cch still exists.
        self.assertEqual(sc.spike_time_tiling_coefficient, sc.sttc)


if __name__ == '__main__':
    unittest.main()