def setUp(self):
self.asiga0 = AnalogSignalArray(np.array(
[np.sin(np.arange(0, 20 * math.pi, 0.1))]).T,
units='mV',
sampling_rate=10 / ms)
self.asiga1 = AnalogSignalArray(np.array([
np.sin(np.arange(0, 20 * math.pi, 0.1)),
np.cos(np.arange(0, 20 * math.pi, 0.1))
]).T,
units='mV',
sampling_rate=10 / ms)
self.asiga2 = AnalogSignalArray(np.array([
np.sin(np.arange(0, 20 * math.pi, 0.1)),
np.cos(np.arange(0, 20 * math.pi, 0.1)),
np.tan(np.arange(0, 20 * math.pi, 0.1))
]).T,
units='mV',
sampling_rate=10 / ms)
self.st0 = SpikeTrain(
[9 * math.pi, 10 * math.pi, 11 * math.pi, 12 * math.pi],
units='ms',
t_stop=self.asiga0.t_stop)
self.lst = [
SpikeTrain([9 * math.pi, 10 * math.pi, 11 * math.pi, 12 * math.pi],
units='ms',
t_stop=self.asiga1.t_stop),
SpikeTrain([30, 35, 40], units='ms', t_stop=self.asiga1.t_stop)
]
def test_one_spiketrain_empty(self):
'''Test for one empty SpikeTrain, but existing spikes in other'''
st = [SpikeTrain(
[9 * math.pi, 10 * math.pi, 11 * math.pi, 12 * math.pi],
units='ms', t_stop=self.asiga1.t_stop),
SpikeTrain([], units='ms', t_stop=self.asiga1.t_stop)]
STA = sta.spike_triggered_average(self.asiga1, st, (-1 * ms, 1 * ms))
cmp_array = AnalogSignalArray(np.array([np.zeros(20, dtype=float)]).T,
units='mV', sampling_rate=10 / ms)
cmp_array = cmp_array / 0.
cmp_array.t_start = -1 * ms
assert_array_equal(STA[:, 1], cmp_array[:, 0])
def test_one_spiketrain_empty(self):
'''Test for one empty SpikeTrain, but existing spikes in other'''
st = [SpikeTrain(
[9 * math.pi, 10 * math.pi, 11 * math.pi, 12 * math.pi],
units='ms', t_stop=self.asiga1.t_stop),
SpikeTrain([], units='ms', t_stop=self.asiga1.t_stop)]
STA = sta.spike_triggered_average(self.asiga1, st, (-1 * ms, 1 * ms))
cmp_array = AnalogSignalArray(np.array([np.zeros(20, dtype=float)]).T,
units='mV', sampling_rate=10 / ms)
cmp_array = cmp_array / 0.
cmp_array.t_start = -1 * ms
assert_array_equal(STA[:, 1], cmp_array[:, 0])
def setUp(self):
# standard testsignals
tlen0 = 100 * pq.s
f0 = 20. * pq.Hz
fs0 = 1 * pq.ms
t0 = np.arange(
0, tlen0.rescale(pq.s).magnitude,
fs0.rescale(pq.s).magnitude) * pq.s
self.anasig0 = AnalogSignalArray(
np.sin(2 * np.pi * (f0 * t0).simplified.magnitude),
units=pq.mV, t_start=0 * pq.ms, sampling_period=fs0)
self.st0 = SpikeTrain(
np.arange(0, tlen0.rescale(pq.ms).magnitude, 50) * pq.ms,
t_start=0 * pq.ms, t_stop=tlen0)
self.bst0 = BinnedSpikeTrain(self.st0, binsize=fs0)
# shortened analogsignals
self.anasig1 = self.anasig0.time_slice(1 * pq.s, None)
self.anasig2 = self.anasig0.time_slice(None, 99 * pq.s)
# increased sampling frequency
fs1 = 0.1 * pq.ms
self.anasig3 = AnalogSignalArray(
np.sin(2 * np.pi * (f0 * t0).simplified.magnitude),
units=pq.mV, t_start=0 * pq.ms, sampling_period=fs1)
self.bst1 = BinnedSpikeTrain(
self.st0.time_slice(self.anasig3.t_start, self.anasig3.t_stop),
binsize=fs1)
# analogsignal containing multiple traces
self.anasig4 = AnalogSignalArray(
np.array([
np.sin(2 * np.pi * (f0 * t0).simplified.magnitude),
np.sin(4 * np.pi * (f0 * t0).simplified.magnitude)]).
transpose(),
units=pq.mV, t_start=0 * pq.ms, sampling_period=fs0)
# shortened spike train
self.st3 = SpikeTrain(
np.arange(
(tlen0.rescale(pq.ms).magnitude * .25),
(tlen0.rescale(pq.ms).magnitude * .75), 50) * pq.ms,
t_start=0 * pq.ms, t_stop=tlen0)
self.bst3 = BinnedSpikeTrain(self.st3, binsize=fs0)
self.st4 = SpikeTrain(np.arange(
(tlen0.rescale(pq.ms).magnitude * .25),
(tlen0.rescale(pq.ms).magnitude * .75), 50) * pq.ms,
t_start=5 * fs0, t_stop=tlen0 - 5 * fs0)
self.bst4 = BinnedSpikeTrain(self.st4, binsize=fs0)
# spike train with incompatible binsize
self.bst5 = BinnedSpikeTrain(self.st3, binsize=fs0 * 2.)
# spike train with same binsize as the analog signal, but with
# bin edges not aligned to the time axis of the analog signal
self.bst6 = BinnedSpikeTrain(
self.st3, binsize=fs0, t_start=4.5 * fs0, t_stop=tlen0 - 4.5 * fs0)
def test_all_spiketrains_empty(self):
st = SpikeTrain([], units='ms', t_stop=self.asiga1.t_stop)
with warnings.catch_warnings(record=True) as w:
# Cause all warnings to always be triggered.
warnings.simplefilter("always")
# Trigger warnings.
STA = sta.spike_triggered_average(
self.asiga1, st, (-1 * ms, 1 * ms))
self.assertEqual("No spike at all was either found or used "
"for averaging", str(w[-1].message))
nan_array = np.empty(20)
nan_array.fill(np.nan)
cmp_array = AnalogSignalArray(np.array([nan_array, nan_array]).T,
units='mV', sampling_rate=10 / ms)
assert_array_equal(STA, cmp_array)
def test_spike_triggered_average_with_n_spikes_on_constant_function(self):
'''Signal should average to the input'''
const = 13.8
x = const * np.ones(201)
asiga = AnalogSignalArray(
np.array([x]).T, units='mV', sampling_rate=10 / ms)
st = SpikeTrain([3, 5.6, 7, 7.1, 16, 16.3], units='ms', t_stop=20)
window_starttime = -2 * ms
window_endtime = 2 * ms
STA = sta.spike_triggered_average(
asiga, st, (window_starttime, window_endtime))
a = int(((window_endtime - window_starttime) *
asiga.sampling_rate).simplified)
cutout = asiga[0: a]
cutout.t_start = window_starttime
assert_array_almost_equal(STA, cutout, 12)
def test_only_one_spike(self):
'''The output should be the same as the input'''
x = np.arange(0, 20, 0.1)
y = x**2
sr = 10 / ms
z = AnalogSignalArray(np.array([y]).T, units='mV', sampling_rate=sr)
spiketime = 8 * ms
spiketime_in_ms = int((spiketime / ms).simplified)
st = SpikeTrain([spiketime_in_ms], units='ms', t_stop=20)
window_starttime = -3 * ms
window_endtime = 5 * ms
STA = sta.spike_triggered_average(
z, st, (window_starttime, window_endtime))
cutout = z[int(((spiketime + window_starttime) * sr).simplified):
int(((spiketime + window_endtime) * sr).simplified)]
cutout.t_start = window_starttime
assert_array_equal(STA, cutout)
def setUp(self):
# standard testsignals
tlen0 = 100 * pq.s
f0 = 20. * pq.Hz
fs0 = 1 * pq.ms
t0 = np.arange(0,
tlen0.rescale(pq.s).magnitude,
fs0.rescale(pq.s).magnitude) * pq.s
self.anasig0 = AnalogSignalArray(np.sin(
2 * np.pi * (f0 * t0).simplified.magnitude),
units=pq.mV,
t_start=0 * pq.ms,
sampling_period=fs0)
self.st0 = SpikeTrain(np.arange(0,
tlen0.rescale(pq.ms).magnitude, 50) *
pq.ms,
t_start=0 * pq.ms,
t_stop=tlen0)
self.bst0 = BinnedSpikeTrain(self.st0, binsize=fs0)
def test_old_scipy_version(self):
self.assertRaises(AttributeError, sta.spike_field_coherence,
self.anasig0, self.bst0)
class sfc_TestCase_new_scipy(unittest.TestCase):
def setUp(self):
# standard testsignals
tlen0 = 100 * pq.s
f0 = 20. * pq.Hz
fs0 = 1 * pq.ms
t0 = np.arange(
0, tlen0.rescale(pq.s).magnitude,
fs0.rescale(pq.s).magnitude) * pq.s
self.anasig0 = AnalogSignalArray(
np.sin(2 * np.pi * (f0 * t0).simplified.magnitude),
units=pq.mV, t_start=0 * pq.ms, sampling_period=fs0)
self.st0 = SpikeTrain(
np.arange(0, tlen0.rescale(pq.ms).magnitude, 50) * pq.ms,
t_start=0 * pq.ms, t_stop=tlen0)
self.bst0 = BinnedSpikeTrain(self.st0, binsize=fs0)
# shortened analogsignals
self.anasig1 = self.anasig0.time_slice(1 * pq.s, None)
self.anasig2 = self.anasig0.time_slice(None, 99 * pq.s)
# increased sampling frequency
fs1 = 0.1 * pq.ms
self.anasig3 = AnalogSignalArray(
np.sin(2 * np.pi * (f0 * t0).simplified.magnitude),
units=pq.mV, t_start=0 * pq.ms, sampling_period=fs1)
self.bst1 = BinnedSpikeTrain(
self.st0.time_slice(self.anasig3.t_start, self.anasig3.t_stop),
binsize=fs1)
# analogsignal containing multiple traces
self.anasig4 = AnalogSignalArray(
np.array([
np.sin(2 * np.pi * (f0 * t0).simplified.magnitude),
np.sin(4 * np.pi * (f0 * t0).simplified.magnitude)]).
transpose(),
units=pq.mV, t_start=0 * pq.ms, sampling_period=fs0)
# shortened spike train
self.st3 = SpikeTrain(
np.arange(
(tlen0.rescale(pq.ms).magnitude * .25),
(tlen0.rescale(pq.ms).magnitude * .75), 50) * pq.ms,
t_start=0 * pq.ms, t_stop=tlen0)
self.bst3 = BinnedSpikeTrain(self.st3, binsize=fs0)
self.st4 = SpikeTrain(np.arange(
(tlen0.rescale(pq.ms).magnitude * .25),
(tlen0.rescale(pq.ms).magnitude * .75), 50) * pq.ms,
t_start=5 * fs0, t_stop=tlen0 - 5 * fs0)
self.bst4 = BinnedSpikeTrain(self.st4, binsize=fs0)
# spike train with incompatible binsize
self.bst5 = BinnedSpikeTrain(self.st3, binsize=fs0 * 2.)
# spike train with same binsize as the analog signal, but with
# bin edges not aligned to the time axis of the analog signal
self.bst6 = BinnedSpikeTrain(
self.st3, binsize=fs0, t_start=4.5 * fs0, t_stop=tlen0 - 4.5 * fs0)
# =========================================================================
# Tests for correct input handling
# =========================================================================
def test_wrong_input_type(self):
self.assertRaises(TypeError,
sta.spike_field_coherence,
np.array([1, 2, 3]), self.bst0)
self.assertRaises(TypeError,
sta.spike_field_coherence,
self.anasig0, [1, 2, 3])
self.assertRaises(ValueError,
sta.spike_field_coherence,
self.anasig0.duplicate_with_new_array([]), self.bst0)
def test_start_stop_times_out_of_range(self):
self.assertRaises(ValueError,
sta.spike_field_coherence,
self.anasig1, self.bst0)
self.assertRaises(ValueError,
sta.spike_field_coherence,
self.anasig2, self.bst0)
def test_non_matching_input_binning(self):
self.assertRaises(ValueError,
sta.spike_field_coherence,
self.anasig0, self.bst1)
def test_incompatible_spiketrain_analogsignal(self):
# These spike trains have incompatible binning (binsize or alignment to
# time axis of analog signal)
self.assertRaises(ValueError,
sta.spike_field_coherence,
self.anasig0, self.bst5)
self.assertRaises(ValueError,
sta.spike_field_coherence,
self.anasig0, self.bst6)
def test_signal_dimensions(self):
# single analogsignal trace and single spike train
s_single, f_single = sta.spike_field_coherence(self.anasig0, self.bst0)
self.assertEqual(len(f_single.shape), 1)
self.assertEqual(len(s_single.shape), 1)
# multiple analogsignal traces and single spike train
s_multi, f_multi = sta.spike_field_coherence(self.anasig4, self.bst0)
self.assertEqual(len(f_multi.shape), 1)
self.assertEqual(len(s_multi.shape), 2)
# frequencies are identical since same sampling frequency was used
# in both cases and data length is the same
assert_array_equal(f_single, f_multi)
# coherences of s_single and first signal in s_multi are identical,
# since first analogsignal trace in anasig4 is same as in anasig0
assert_array_equal(s_single, s_multi[:, 0])
def test_non_binned_spiketrain_input(self):
s, f = sta.spike_field_coherence(self.anasig0, self.st0)
f_ind = np.where(f >= 19.)[0][0]
max_ind = np.argmax(s[1:]) + 1
self.assertEqual(f_ind, max_ind)
self.assertAlmostEqual(s[f_ind], 1., delta=0.01)
# =========================================================================
# Tests for correct return values
# =========================================================================
def test_spike_field_coherence_perfect_coherence(self):
# check for detection of 20Hz peak in anasig0/bst0
s, f = sta.spike_field_coherence(
self.anasig0, self.bst0, window='boxcar')
f_ind = np.where(f >= 19.)[0][0]
max_ind = np.argmax(s[1:]) + 1
self.assertEqual(f_ind, max_ind)
self.assertAlmostEqual(s[f_ind], 1., delta=0.01)
def test_output_frequencies(self):
nfft = 256
_, f = sta.spike_field_coherence(self.anasig3, self.bst1, nfft=nfft)
# check number of frequency samples
self.assertEqual(len(f), nfft / 2 + 1)
# check values of frequency samples
assert_array_almost_equal(
f, np.linspace(
0, self.anasig3.sampling_rate.rescale('Hz').magnitude / 2,
nfft / 2 + 1) * pq.Hz)
def test_short_spiketrain(self):
# this spike train has the same length as anasig0
s1, f1 = sta.spike_field_coherence(
self.anasig0, self.bst3, window='boxcar')
# this spike train has the same spikes as above, but is shorter than
# anasig0
s2, f2 = sta.spike_field_coherence(
self.anasig0, self.bst4, window='boxcar')
# the results above should be the same, nevertheless
assert_array_equal(s1.magnitude, s2.magnitude)
assert_array_equal(f1.magnitude, f2.magnitude)
def test_empty_analogsignal(self):
asiga = AnalogSignalArray([], units='mV', sampling_rate=10 / ms)
st = SpikeTrain([5], units='ms', t_stop=10)
self.assertRaises(ValueError, sta.spike_triggered_average,
asiga, st, (-1 * ms, 1 * ms))
def setUp(self):
# standard testsignals
tlen0 = 100 * pq.s
f0 = 20. * pq.Hz
fs0 = 1 * pq.ms
t0 = np.arange(0,
tlen0.rescale(pq.s).magnitude,
fs0.rescale(pq.s).magnitude) * pq.s
self.anasig0 = AnalogSignalArray(np.sin(
2 * np.pi * (f0 * t0).simplified.magnitude),
units=pq.mV,
t_start=0 * pq.ms,
sampling_period=fs0)
self.st0 = SpikeTrain(np.arange(0,
tlen0.rescale(pq.ms).magnitude, 50) *
pq.ms,
t_start=0 * pq.ms,
t_stop=tlen0)
self.bst0 = BinnedSpikeTrain(self.st0, binsize=fs0)
# shortened analogsignals
self.anasig1 = self.anasig0.time_slice(1 * pq.s, None)
self.anasig2 = self.anasig0.time_slice(None, 99 * pq.s)
# increased sampling frequency
fs1 = 0.1 * pq.ms
self.anasig3 = AnalogSignalArray(np.sin(
2 * np.pi * (f0 * t0).simplified.magnitude),
units=pq.mV,
t_start=0 * pq.ms,
sampling_period=fs1)
self.bst1 = BinnedSpikeTrain(self.st0.time_slice(
self.anasig3.t_start, self.anasig3.t_stop),
binsize=fs1)
# analogsignal containing multiple traces
self.anasig4 = AnalogSignalArray(np.array([
np.sin(2 * np.pi * (f0 * t0).simplified.magnitude),
np.sin(4 * np.pi * (f0 * t0).simplified.magnitude)
]).transpose(),
units=pq.mV,
t_start=0 * pq.ms,
sampling_period=fs0)
# shortened spike train
self.st3 = SpikeTrain(np.arange(
(tlen0.rescale(pq.ms).magnitude * .25),
(tlen0.rescale(pq.ms).magnitude * .75), 50) * pq.ms,
t_start=0 * pq.ms,
t_stop=tlen0)
self.bst3 = BinnedSpikeTrain(self.st3, binsize=fs0)
self.st4 = SpikeTrain(np.arange(
(tlen0.rescale(pq.ms).magnitude * .25),
(tlen0.rescale(pq.ms).magnitude * .75), 50) * pq.ms,
t_start=5 * fs0,
t_stop=tlen0 - 5 * fs0)
self.bst4 = BinnedSpikeTrain(self.st4, binsize=fs0)
# spike train with incompatible binsize
self.bst5 = BinnedSpikeTrain(self.st3, binsize=fs0 * 2.)
# spike train with same binsize as the analog signal, but with
# bin edges not aligned to the time axis of the analog signal
self.bst6 = BinnedSpikeTrain(self.st3,
binsize=fs0,
t_start=4.5 * fs0,
t_stop=tlen0 - 4.5 * fs0)
class sfc_TestCase_new_scipy(unittest.TestCase):
def setUp(self):
# standard testsignals
tlen0 = 100 * pq.s
f0 = 20. * pq.Hz
fs0 = 1 * pq.ms
t0 = np.arange(0,
tlen0.rescale(pq.s).magnitude,
fs0.rescale(pq.s).magnitude) * pq.s
self.anasig0 = AnalogSignalArray(np.sin(
2 * np.pi * (f0 * t0).simplified.magnitude),
units=pq.mV,
t_start=0 * pq.ms,
sampling_period=fs0)
self.st0 = SpikeTrain(np.arange(0,
tlen0.rescale(pq.ms).magnitude, 50) *
pq.ms,
t_start=0 * pq.ms,
t_stop=tlen0)
self.bst0 = BinnedSpikeTrain(self.st0, binsize=fs0)
# shortened analogsignals
self.anasig1 = self.anasig0.time_slice(1 * pq.s, None)
self.anasig2 = self.anasig0.time_slice(None, 99 * pq.s)
# increased sampling frequency
fs1 = 0.1 * pq.ms
self.anasig3 = AnalogSignalArray(np.sin(
2 * np.pi * (f0 * t0).simplified.magnitude),
units=pq.mV,
t_start=0 * pq.ms,
sampling_period=fs1)
self.bst1 = BinnedSpikeTrain(self.st0.time_slice(
self.anasig3.t_start, self.anasig3.t_stop),
binsize=fs1)
# analogsignal containing multiple traces
self.anasig4 = AnalogSignalArray(np.array([
np.sin(2 * np.pi * (f0 * t0).simplified.magnitude),
np.sin(4 * np.pi * (f0 * t0).simplified.magnitude)
]).transpose(),
units=pq.mV,
t_start=0 * pq.ms,
sampling_period=fs0)
# shortened spike train
self.st3 = SpikeTrain(np.arange(
(tlen0.rescale(pq.ms).magnitude * .25),
(tlen0.rescale(pq.ms).magnitude * .75), 50) * pq.ms,
t_start=0 * pq.ms,
t_stop=tlen0)
self.bst3 = BinnedSpikeTrain(self.st3, binsize=fs0)
self.st4 = SpikeTrain(np.arange(
(tlen0.rescale(pq.ms).magnitude * .25),
(tlen0.rescale(pq.ms).magnitude * .75), 50) * pq.ms,
t_start=5 * fs0,
t_stop=tlen0 - 5 * fs0)
self.bst4 = BinnedSpikeTrain(self.st4, binsize=fs0)
# spike train with incompatible binsize
self.bst5 = BinnedSpikeTrain(self.st3, binsize=fs0 * 2.)
# spike train with same binsize as the analog signal, but with
# bin edges not aligned to the time axis of the analog signal
self.bst6 = BinnedSpikeTrain(self.st3,
binsize=fs0,
t_start=4.5 * fs0,
t_stop=tlen0 - 4.5 * fs0)
# =========================================================================
# Tests for correct input handling
# =========================================================================
def test_wrong_input_type(self):
self.assertRaises(TypeError, sta.spike_field_coherence,
np.array([1, 2, 3]), self.bst0)
self.assertRaises(TypeError, sta.spike_field_coherence, self.anasig0,
[1, 2, 3])
self.assertRaises(ValueError, sta.spike_field_coherence,
self.anasig0.duplicate_with_new_array([]), self.bst0)
def test_start_stop_times_out_of_range(self):
self.assertRaises(ValueError, sta.spike_field_coherence, self.anasig1,
self.bst0)
self.assertRaises(ValueError, sta.spike_field_coherence, self.anasig2,
self.bst0)
def test_non_matching_input_binning(self):
self.assertRaises(ValueError, sta.spike_field_coherence, self.anasig0,
self.bst1)
def test_incompatible_spiketrain_analogsignal(self):
# These spike trains have incompatible binning (binsize or alignment to
# time axis of analog signal)
self.assertRaises(ValueError, sta.spike_field_coherence, self.anasig0,
self.bst5)
self.assertRaises(ValueError, sta.spike_field_coherence, self.anasig0,
self.bst6)
def test_signal_dimensions(self):
# single analogsignal trace and single spike train
s_single, f_single = sta.spike_field_coherence(self.anasig0, self.bst0)
self.assertEqual(len(f_single.shape), 1)
self.assertEqual(len(s_single.shape), 1)
# multiple analogsignal traces and single spike train
s_multi, f_multi = sta.spike_field_coherence(self.anasig4, self.bst0)
self.assertEqual(len(f_multi.shape), 1)
self.assertEqual(len(s_multi.shape), 2)
# frequencies are identical since same sampling frequency was used
# in both cases and data length is the same
assert_array_equal(f_single, f_multi)
# coherences of s_single and first signal in s_multi are identical,
# since first analogsignal trace in anasig4 is same as in anasig0
assert_array_equal(s_single, s_multi[:, 0])
def test_non_binned_spiketrain_input(self):
s, f = sta.spike_field_coherence(self.anasig0, self.st0)
f_ind = np.where(f >= 19.)[0][0]
max_ind = np.argmax(s[1:]) + 1
self.assertEqual(f_ind, max_ind)
self.assertAlmostEqual(s[f_ind], 1., delta=0.01)
# =========================================================================
# Tests for correct return values
# =========================================================================
def test_spike_field_coherence_perfect_coherence(self):
# check for detection of 20Hz peak in anasig0/bst0
s, f = sta.spike_field_coherence(self.anasig0,
self.bst0,
window='boxcar')
f_ind = np.where(f >= 19.)[0][0]
max_ind = np.argmax(s[1:]) + 1
self.assertEqual(f_ind, max_ind)
self.assertAlmostEqual(s[f_ind], 1., delta=0.01)
def test_output_frequencies(self):
nfft = 256
_, f = sta.spike_field_coherence(self.anasig3, self.bst1, nfft=nfft)
# check number of frequency samples
self.assertEqual(len(f), nfft / 2 + 1)
# check values of frequency samples
assert_array_almost_equal(
f,
np.linspace(0,
self.anasig3.sampling_rate.rescale('Hz').magnitude / 2,
nfft / 2 + 1) * pq.Hz)
def test_short_spiketrain(self):
# this spike train has the same length as anasig0
s1, f1 = sta.spike_field_coherence(self.anasig0,
self.bst3,
window='boxcar')
# this spike train has the same spikes as above, but is shorter than
# anasig0
s2, f2 = sta.spike_field_coherence(self.anasig0,
self.bst4,
window='boxcar')
# the results above should be the same, nevertheless
assert_array_equal(s1.magnitude, s2.magnitude)
assert_array_equal(f1.magnitude, f2.magnitude)
