def setUp(self):
xx_ele, yy_ele, zz_ele = utils.generate_electrodes(dim=3, res=5,
xlims=[0.15, 0.85],
ylims=[0.15, 0.85],
zlims=[0.15, 0.85])
self.ele_pos = np.vstack((xx_ele, yy_ele, zz_ele)).T
self.csd_profile = utils.gauss_3d_dipole
pots = CSD.generate_lfp(self.csd_profile, xx_ele, yy_ele, zz_ele)
self.pots = np.reshape(pots, (-1, 1))
self.test_method = 'KCSD3D'
self.test_params = {'gdx': 0.05, 'gdy': 0.05, 'gdz': 0.05,
'lambd': 5.10896977451e-19, 'src_type': 'step',
'R_init': 0.31, 'xmin': 0., 'xmax': 1., 'ymin': 0.,
'ymax': 1., 'zmin': 0., 'zmax': 1.}
temp_signals = []
for ii in range(len(self.pots)):
temp_signals.append(self.pots[ii])
self.an_sigs = neo.AnalogSignal(np.array(temp_signals).T * pq.mV,
sampling_rate=1000 * pq.Hz)
self.an_sigs.annotate(coordinates=self.ele_pos * pq.mm)
def test_traceviewer_cls_method_neo(interactive=False):
sigs = np.random.rand(100000,16)
sample_rate = 1000.
t_start = 0.
neo_anasig = neo.AnalogSignal(sigs*pq.mV, sampling_rate=sample_rate*pq.Hz, t_start=0*pq.s, copy=True)
print(neo_anasig)
app = ephyviewer.mkQApp()
view = ephyviewer.TraceViewer.from_neo_analogsignal(neo_anasig, 'sigs')
win = ephyviewer.MainViewer(debug=True, show_auto_scale=True)
win.add_view(view)
if interactive:
win.show()
app.exec_()
else:
# close thread properly
win.close()
def test_threshold_detection(self):
# Test whether spikes are extracted at the correct times from
# an analog signal.
# Load membrane potential simulated using Brian2
# according to make_spike_extraction_test_data.py.
curr_dir = os.path.dirname(os.path.realpath(__file__))
raw_data_file_loc = os.path.join(curr_dir,
'spike_extraction_test_data.txt')
raw_data = []
with open(raw_data_file_loc, 'r') as f:
for x in (f.readlines()):
raw_data.append(float(x))
vm = neo.AnalogSignal(raw_data, units=V, sampling_period=0.1 * ms)
spike_train = stgen.threshold_detection(vm)
try:
len(spike_train)
except TypeError: # Handles an error in Neo related to some zero length
# spike trains being treated as unsized objects.
warnings.warn((
"The spike train may be an unsized object. This may be related "
"to an issue in Neo with some zero-length SpikeTrain objects. "
"Bypassing this by creating an empty SpikeTrain object."))
spike_train = neo.core.SpikeTrain([],
t_start=spike_train.t_start,
t_stop=spike_train.t_stop,
units=spike_train.units)
# Correct values determined previously.
true_spike_train = [
0.0123, 0.0354, 0.0712, 0.1191, 0.1694, 0.22, 0.2711
]
# Does threshold_detection gives the correct number of spikes?
self.assertEqual(len(spike_train), len(true_spike_train))
# Does threshold_detection gives the correct times for the spikes?
try:
assert_array_almost_equal(spike_train, spike_train)
except AttributeError: # If numpy version too old to have allclose
self.assertTrue(np.array_equal(spike_train, spike_train))
def spikes(instantaneous_rates, num_trials, timestep):
"""
Parameters
----------
instantaneous_rates : np.ndarray
Array containing time series.
timestep :
Sample period.
num_steps : int
Number of timesteps -> max_time = timestep*(num_steps-1).
Returns
-------
spiketrains : list of neo.SpikeTrains
List containing spiketrains of inhomogeneous Poisson
processes based on given instantaneous rates.
"""
spiketrains = []
for _ in range(num_trials):
spiketrains_per_trial = []
for inst_rate in instantaneous_rates:
# print(inst_rate.shape)
# print(inst_rate)
anasig_inst_rate = neo.AnalogSignal(inst_rate,
sampling_rate=1 /
timestep,
units=pq.Hz)
# print(inst_rate)
# print(np.mean(inst_rate))
# print(anasig_inst_rate)
spiketrains_per_trial.append(
inhomogeneous_poisson_process(anasig_inst_rate,
as_array=True))
# print(spiketrains_per_trial)
# assert False
spiketrains.append(spiketrains_per_trial)
return spiketrains
def test_recovered_firing_rate_profile(self):
np.random.seed(54)
t_start = 0 * pq.s
t_stop = 4 * np.round(np.pi, decimals=3) * pq.s # 2 full periods
sampling_period = 0.001 * pq.s
# an arbitrary rate profile
profile = 0.5 * (1 + np.sin(np.arange(t_start.item(), t_stop.item(),
sampling_period.item())))
time_generation = 0
n_trials = 200
rtol = 0.05 # 5% of deviation allowed
kernel = kernels.RectangularKernel(sigma=0.25 * pq.s)
for rate in (10 * pq.Hz, 100 * pq.Hz):
rate_profile = neo.AnalogSignal(rate * profile,
sampling_period=sampling_period)
# the recovered firing rate profile should not depend on the
# shape factor; here we test float and integer values of the shape
# factor: the method supports float values that is not trivial
# for inhomogeneous gamma process generation
for shape_factor in (1, 2.5, 10.):
spiketrains = \
[stgen.inhomogeneous_gamma_process(
rate_profile, shape_factor=shape_factor)
for _ in range(n_trials)]
rate_recovered = instantaneous_rate(
spiketrains,
sampling_period=sampling_period,
kernel=kernel,
t_start=t_start,
t_stop=t_stop, trim=True).sum(axis=1) / n_trials
rate_recovered = rate_recovered.flatten().magnitude
trim = (rate_profile.shape[0] - rate_recovered.shape[0]) // 2
rate_profile_valid = rate_profile.magnitude.squeeze()
rate_profile_valid = rate_profile_valid[trim: -trim - 1]
assert_allclose(rate_recovered, rate_profile_valid,
rtol=0, atol=rtol * rate.item())
def setUp(self):
xx_ele, yy_ele = utils.generate_electrodes(dim=2, res=9,
xlims=[0.05, 0.95],
ylims=[0.05, 0.95])
self.ele_pos = np.vstack((xx_ele, yy_ele)).T
self.csd_profile = utils.large_source_2D
pots = CSD.generate_lfp(
self.csd_profile,
xx_ele,
yy_ele,
resolution=100)
self.pots = np.reshape(pots, (-1, 1))
self.test_method = 'KCSD2D'
self.test_params = {'gdx': 0.25, 'gdy': 0.25, 'R_init': 0.08,
'h': 50., 'xmin': 0., 'xmax': 1.,
'ymin': 0., 'ymax': 1.}
temp_signals = []
for ii in range(len(self.pots)):
temp_signals.append(self.pots[ii])
self.an_sigs = neo.AnalogSignal(np.array(temp_signals).T * pq.mV,
sampling_rate=1000 * pq.Hz)
self.an_sigs.annotate(coordinates=self.ele_pos * pq.mm)
def read(self, block_index=0):
"""read neo block from hdf5 file
Parameters
----------
block_index : int
Index of the block in the file. Defaults to 0.
"""
# Read neo block
reader = neo.io.NeoHdf5IO()
reader.connect(self.h5_file)
block = reader.read()[block_index]
reader.close()
# Add wav file data to block
h5path = split(self.h5_file)[0]
for seg in block.segments:
# Read data from wav file
wav_file = seg.annotations['wav_file']
wav_path = join(h5path, 'wav', wav_file)
fs, data = wavfile.read(wav_path)
# Convert int16 to normalized float64
data = data / 2.**15
# Add samples to segment
sig = neo.AnalogSignal(data,
copy=False,
units=pq.V,
t_start=0 * pq.s,
file_origin=wav_file,
sampling_rate=fs * pq.Hz,
name=splitext(wav_file)[0])
seg.annotate(wav_signal=sig)
return block
def test_zscore_single_inplace_int(self):
"""
Test if the z-score is correctly calculated even if the input is an
AnalogSignal of type int, asking for an inplace operation.
"""
signal = neo.AnalogSignal(self.test_seq1,
units='mV',
t_start=0. * pq.ms,
sampling_rate=1000. * pq.Hz,
dtype=int)
m = np.mean(self.test_seq1)
s = np.std(self.test_seq1)
target = (self.test_seq1 - m) / s
assert_array_almost_equal(elephant.signal_processing.zscore(
signal, inplace=True).magnitude,
target.reshape(-1, 1).astype(int),
decimal=9)
# Assert original signal is overwritten
self.assertEqual(signal[0].magnitude, target.astype(int)[0])
def test_zscore_single_inplace(self):
"""
Test z-score on a single AnalogSignal, asking for an inplace
operation.
"""
signal = neo.AnalogSignal(
self.test_seq1, units='mV',
t_start=0. * pq.ms, sampling_rate=1000. * pq.Hz, dtype=float)
m = np.mean(self.test_seq1)
s = np.std(self.test_seq1)
target = (self.test_seq1 - m) / s
result = elephant.signal_processing.zscore(signal, inplace=True)
assert_array_almost_equal(
result.magnitude, target.reshape(-1, 1), decimal=9)
self.assertEqual(result.units, pq.Quantity(1. * pq.dimensionless))
# Assert original signal is overwritten
self.assertEqual(signal[0].magnitude, target[0])
def test_zscore_single_dup(self):
"""
Test z-score on a single AnalogSignal, asking to return a
duplicate.
"""
signal = neo.AnalogSignal(
self.test_seq1, units='mV',
t_start=0. * pq.ms, sampling_rate=1000. * pq.Hz, dtype=float)
m = np.mean(self.test_seq1)
s = np.std(self.test_seq1)
target = (self.test_seq1 - m) / s
assert_array_equal(target, scipy.stats.zscore(self.test_seq1))
result = elephant.signal_processing.zscore(signal, inplace=False)
assert_array_almost_equal(
result.magnitude, target.reshape(-1, 1), decimal=9)
self.assertEqual(result.units, pq.Quantity(1. * pq.dimensionless))
# Assert original signal is untouched
self.assertEqual(signal[0].magnitude, self.test_seq1[0])
def test_zscore_single_multidim_inplace(self):
"""
Test z-score on a single AnalogSignal with multiple dimensions, asking
for an inplace operation.
"""
signal = neo.AnalogSignal(np.vstack([self.test_seq1, self.test_seq2]),
units='mV',
t_start=0. * pq.ms,
sampling_rate=1000. * pq.Hz,
dtype=float)
m = np.mean(signal.magnitude, axis=0, keepdims=True)
s = np.std(signal.magnitude, axis=0, keepdims=True)
target = (signal.magnitude - m) / s
assert_array_almost_equal(elephant.signal_processing.zscore(
signal, inplace=True).magnitude,
target,
decimal=9)
# Assert original signal is overwritten
self.assertEqual(signal[0, 0].magnitude, target[0, 0])
def analog_signal_array_to_analog_signals(signal_array):
""" Return a list of analog signals for an analog signal array.
If ``signal_array`` is attached to a recording channel group with exactly
is many channels as there are channels in ``signal_array``, each created
signal will be assigned the corresponding channel. If the attached
recording channel group has only one recording channel, all created signals
will be assigned to this channel. In all other cases, the created
signal will not have a reference to a recording channel.
Note that while the created signals may have references to a segment and
channels, the relationships in the other direction are
not automatically created (the signals are not attached to the recording
channel or segment). Other properties like annotations are not copied or
referenced in the created analog signals.
:param signal_array: An analog signal array from which the
:class:`neo.core.AnalogSignal` objects are constructed.
:type signal_array: :class:`neo.core.AnalogSignalArray`
:return: A list of analog signals, one for every channel in
``signal_array``.
:rtype: list
"""
signals = []
rcg = signal_array.recordingchannelgroup
for i in xrange(signal_array.shape[1]):
s = neo.AnalogSignal(signal_array[:, i],
t_start=signal_array.t_start,
sampling_rate=signal_array.sampling_rate)
if len(rcg.recordingchannels) == 1:
s.recordingchannel = rcg.recordingchannels[0]
elif len(rcg.recordingchannels) == signal_array.shape[1]:
s.recordingchannel = rcg.recordingchannels[i]
s.segment = signal_array.segment
signals.append(s)
return signals
def ApplyFilter(self, sig):
st = np.array(sig)
for nf, typ in enumerate(self.Type):
if typ == 'lp' or typ == 'hp':
FType = self.FTypes[typ]
Freqs = self.Freq1[nf]/(0.5*sig.sampling_rate)
elif typ == 'bp' or typ == 'bs':
FType = self.FTypes[typ]
Freqs = [self.Freq1[nf]/(0.5*sig.sampling_rate),
self.Freq2[nf]/(0.5*sig.sampling_rate)]
else:
print 'Filter Type error ', typ
continue
# print nf, self.Order[nf], Freqs, FType
b, a = signal.butter(self.Order[nf], Freqs, FType)
st = signal.filtfilt(b, a, st, axis=0)
return neo.AnalogSignal(st,
units=sig.units,
t_start=sig.t_start,
sampling_rate=sig.sampling_rate,
name=sig.name)
def InitContMeas(self, Vin, Fs, Refresh, RecDC=True, GenTestSig=False):
# Init Neo record
out_seg = neo.Segment(name='NewSeg')
if RecDC:
self.EventContDcDone = self.ContDcDoneCallback
for chk, chi, in sorted(self.DCChannelIndex.iteritems()):
name = chk
sig = neo.AnalogSignal(signal=np.empty((0, 1), float),
units=pq.V,
t_start=0*pq.s,
sampling_rate=Fs*pq.Hz,
name=name)
out_seg.analogsignals.append(sig)
self.ContRecord = NeoRecord(Seg=out_seg, UnitGain=1)
# Lauch adquisition
self.SetBias(Vsig=Vin)
self.GetContinuousCurrent(Fs=Fs,
Refresh=Refresh,
GenTestSig=GenTestSig)
self.CharactRunning = True
def test_cross_correlation_freqs(self):
'''
Sine vs cosine for different frequencies
Note, that accuracy depends on N and min(f).
E.g., f=0.1 and N=2018 only has an accuracy on the order decimal=1
'''
freq_arr = np.linspace(0.5, 15, 8) * pq.Hz
signal = np.zeros((self.n_samples, 2))
for freq in freq_arr:
signal[:, 0] = np.sin(2. * np.pi * freq * self.time)
signal[:, 1] = np.cos(2. * np.pi * freq * self.time)
# Convert signal to neo.AnalogSignal
signal_neo = neo.AnalogSignal(signal,
units='mV',
t_start=0. * pq.ms,
sampling_rate=self.sampling_rate,
dtype=float)
rho = elephant.signal_processing.cross_correlation_function(
signal_neo, [0, 1])
# Cross-correlation of sine and cosine should be sine
assert_array_almost_equal(rho.magnitude[:, 0],
np.sin(2. * np.pi * freq * rho.times),
decimal=2)
def test_zscore_single_dup_int(self):
"""
Test if the z-score is correctly calculated even if the input is an
AnalogSignal of type int, asking for a duplicate (duplicate should
be of type float).
"""
signal = neo.AnalogSignal(self.test_seq1,
units='mV',
t_start=0. * pq.ms,
sampling_rate=1000. * pq.Hz,
dtype=int)
m = np.mean(self.test_seq1)
s = np.std(self.test_seq1)
target = (self.test_seq1 - m) / s
assert_array_almost_equal(elephant.signal_processing.zscore(
signal, inplace=False).magnitude,
target.reshape(-1, 1),
decimal=9)
# Assert original signal is untouched
self.assertEqual(signal.magnitude[0], self.test_seq1[0])
def test_zscore_single_multidim_dup(self):
'''
Test z-score on a single AnalogSignal with multiple dimensions, asking
to return a duplicate.
'''
signal = neo.AnalogSignal(np.transpose(
np.vstack([self.test_seq1, self.test_seq2])),
units='mV',
t_start=0. * pq.ms,
sampling_rate=1000. * pq.Hz,
dtype=float)
m = np.mean(signal.magnitude, axis=0, keepdims=True)
s = np.std(signal.magnitude, axis=0, keepdims=True)
target = (signal.magnitude - m) / s
assert_array_almost_equal(elephant.signal_processing.zscore(
signal, inplace=False).magnitude,
target,
decimal=9)
# Assert original signal is untouched
self.assertEqual(signal[0, 0].magnitude, self.test_seq1[0])
def runtest_neo(io, N):
times = []
blk = neo.Block()
seg = neo.Segment()
blk.segments.append(seg)
step = 1
if N >= 10:
step = N // 10
Ns = list()
for n in range(0, N + step, step):
seg.analogsignals = []
for ni in range(n):
seg.analogsignals.append(
neo.AnalogSignal(signal=[0],
units="V",
sampling_rate=1 * pq.Hz))
t0 = time()
io.write_block(blk)
times.append(time() - t0)
Ns.append(n)
print(f" :: {n}/{N} {int(n/N*100):3d}%", end="\r")
print(f" :: Last write time: {times[-1]:7.05f} s")
print("Verifying neo-nix file")
assert len(io.nix_file.blocks) == 1
blk = io.nix_file.blocks[0]
assert blk.type == "neo.block"
assert len(blk.groups) == 1
grp = blk.groups[0]
assert grp.type == "neo.segment"
assert len(blk.data_arrays) == N
assert len(grp.data_arrays) == N
return Ns, times
def get_var(blk, varname='M', join=True, keep_neo=True):
''' use this utility to access an analog variable from all segments in a block easily
If you choose to join the segments, returns a list of '''
split_points = []
var = []
# Create a list of the analog signals for each segment
for seg in blk.segments:
names = [str(x.name) for x in seg.analogsignals]
names = [w.replace('Moment', 'M') for w in names]
names = [w.replace('Force', 'F') for w in names]
idx = names.index(varname)
if keep_neo:
var.append(seg.analogsignals[idx])
else:
var.append(seg.analogsignals[idx].as_array())
split_points.append(seg.analogsignals[idx].shape[0])
if join:
if keep_neo:
data = []
t_start = 0. * pq.s
t_stop = 0. * pq.s
for seg in var:
data.append(seg.as_array())
t_stop += seg.t_stop
data = np.concatenate(data, axis=0)
sig = neo.AnalogSignal(data * var[0].units,
t_start=t_start,
sampling_rate=var[0].sampling_rate,
name=var[0].name)
return sig
else:
var = np.concatenate(var, axis=0)
return (var, split_points)
else:
return var
def _cch_speed(binned_st1, binned_st2, left_edge, right_edge, cch_mode,
border_corr, binary, kern):
# Retrieve the array of the binne spike train
st1_arr = binned_st1.to_array()[0, :]
st2_arr = binned_st2.to_array()[0, :]
# Convert the to binary version
if binary:
st1_arr = np.array(st1_arr > 0, dtype=int)
st2_arr = np.array(st2_arr > 0, dtype=int)
if cch_mode == 'pad':
# Zero padding to stay between left_edge and right_edge
st1_arr = np.pad(
st1_arr,
(int(np.abs(np.min([left_edge, 0]))), np.max([right_edge, 0])),
mode='constant')
cch_mode = 'valid'
# Cross correlate the spike trains
counts = np.correlate(st2_arr, st1_arr, mode=cch_mode)
bin_ids = np.r_[left_edge:right_edge + 1]
# Border correction
if border_corr is True:
counts = _border_correction(counts, max_num_bins, left_edge,
right_edge)
if kern is not None:
# Smoothing
counts = _kernel_smoothing(counts, kern, left_edge, right_edge)
# Transform the array count into an AnalogSignal
cch_result = neo.AnalogSignal(signal=counts.reshape(counts.size, 1),
units=pq.dimensionless,
t_start=(bin_ids[0] - 0.5) *
binned_st1.binsize,
sampling_period=binned_st1.binsize)
# Return only the hist_bins bins and counts before and after the
# central one
return cch_result, bin_ids
def test_butter_input_types(self):
# generate white noise data of different types
noise_np = np.random.normal(size=5000)
noise_pq = noise_np * pq.mV
noise = neo.AnalogSignal(noise_pq, sampling_rate=1000.0 * pq.Hz)
# check input as NumPy ndarray
filtered_noise_np = elephant.signal_processing.butter(noise_np,
400.0,
100.0,
fs=1000.0)
self.assertTrue(isinstance(filtered_noise_np, np.ndarray))
self.assertFalse(isinstance(filtered_noise_np, pq.quantity.Quantity))
self.assertFalse(isinstance(filtered_noise_np, neo.AnalogSignal))
self.assertEqual(filtered_noise_np.shape, noise_np.shape)
# check input as Quantity array
filtered_noise_pq = elephant.signal_processing.butter(noise_pq,
400.0 * pq.Hz,
100.0 * pq.Hz,
fs=1000.0)
self.assertTrue(isinstance(filtered_noise_pq, pq.quantity.Quantity))
self.assertFalse(isinstance(filtered_noise_pq, neo.AnalogSignal))
self.assertEqual(filtered_noise_pq.shape, noise_pq.shape)
# check input as neo AnalogSignal
filtered_noise = elephant.signal_processing.butter(
noise, 400.0 * pq.Hz, 100.0 * pq.Hz)
self.assertTrue(isinstance(filtered_noise, neo.AnalogSignal))
self.assertEqual(filtered_noise.shape, noise.shape)
# check if the results from different input types are identical
self.assertTrue(
np.all(filtered_noise_pq.magnitude == filtered_noise_np))
self.assertTrue(
np.all(filtered_noise.magnitude[:, 0] == filtered_noise_np))
def dataArray2AnalogSignal(dataArray):
'''
Convert a nix data_array into a neo analogsignal
:param dataArray: nix.data_array
:return: neo.analogsignal
'''
assert len(
dataArray.dimensions) == 1, 'Only one dimensional arrays are supported'
dim = dataArray.dimensions[0]
assert isinstance(dim, nix.pycore.SampledDimension), 'Only Sampled Dimensions' \
'are supported'
t_start = qu.Quantity(dim.offset, units=dim.unit)
samplingPeriod = qu.Quantity(dim.sampling_interval, units=dim.unit)
analogSignal = neo.AnalogSignal(signal=np.array(dataArray[:]),
units=dataArray.unit,
sampling_period=samplingPeriod,
t_start=t_start)
analogSignal.name = dataArray.name
return analogSignal
def recording(self, port_name, t_start=None):
"""
Return recorded data as a dictionary containing one numpy array for
each neuron, ids as keys.
"""
if self.is_dead():
t_stop = self._t_stop
else:
t_stop = self.Simulation.active().t
if t_start is None:
t_start = UnitHandler.to_pq_quantity(self._t_start)
t_start = pq.Quantity(t_start, 'ms')
t_stop = self.unit_handler.to_pq_quantity(t_stop)
try:
port = self.component_class.port(port_name)
except NineMLNameError:
port = self.component_class.state_variable(port_name)
if isinstance(port, EventPort):
events = numpy.asarray(self._recordings[port_name])
recording = neo.SpikeTrain(self._trim_spike_train(events, t_start),
t_start=t_start,
t_stop=t_stop,
units='ms')
else:
units_str = self.unit_handler.dimension_to_unit_str(
port.dimension, one_as_dimensionless=True)
interval = h.dt * pq.ms
signal = numpy.asarray(self._recordings[port_name])
recording = neo.AnalogSignal(self._trim_analog_signal(
signal, t_start, interval),
sampling_period=interval,
t_start=t_start,
units=units_str,
name=port_name)
recording = recording[:-1] # Drop final timepoint
return recording
def merge_analogsingals(asigs):
# ToDo: to be replaced by neo utils functions
if len(asigs) == 1:
return asigs[0]
min_length = np.min([len(asig.times) for asig in asigs])
max_length = np.max([len(asig.times) for asig in asigs])
if min_length != max_length:
print('Warning: the length of the analog signals differs '\
+ 'between {} and {} '.format(min_length, max_length)\
+ 'All signals will be cut to the same length and merged '\
+ 'into one AnalogSignal object.')
if len(np.unique([asig.sampling_rate for asig in asigs])) > 1:
raise ValueError('The AnalogSignal objects have different '\
+ 'sampling rates!')
asig_array = np.zeros((min_length, len(asigs)))
for channel_number, asig in enumerate(asigs):
asig_array[:,
channel_number] = np.squeeze(asig.as_array()[:min_length])
merged_asig = neo.AnalogSignal(asig_array * asigs[0].units,
sampling_rate=asigs[0].sampling_rate,
t_start=asigs[0].t_start)
for key in asigs[0].annotations.keys():
annotation_values = np.array([a.annotations[key] for a in asigs])
try:
if (annotation_values == annotation_values[0]).all():
merged_asig.annotations[key] = annotation_values[0]
else:
merged_asig.array_annotations[key] = annotation_values
except:
print('Can not merge annotation ', key)
return merged_asig
MultiTimer("generate data")
# =======================================================================
# ASSET Method
# =======================================================================
imat, xx, yy = asset.intersection_matrix(sts, binsize=binsize, dt=T)
MultiTimer("intersection_matrix")
# Compute the probability matrix, either analytically or via bootstrapping
if prob_method == 'a':
# Estimate rates
fir_rates = list(np.zeros(shape=len(sts)))
for st_id, st_trial in enumerate(sts):
fir_rates[st_id] = estats.instantaneous_rate(
st_trial, sampling_period=sampl_period)
fir_rates[st_id] = neo.AnalogSignal(fir_rates[st_id],
t_start=t_pre,
t_stop=t_post,
sampling_period=sampl_period)
# Compute the probability matrix analytically
pmat, x_edges, y_edges = asset.probability_matrix_analytical(
sts, binsize, dt=T, fir_rates=fir_rates)
elif prob_method == 'b':
# Compute the probability matrix via bootstrapping (Montecarlo)
pmat, x_edges, y_edges = asset.probability_matrix_montecarlo(sts,
binsize,
dt=T,
j=dither_T,
n_surr=n_surr)
MultiTimer("prob_method")
# Compute the joint probability matrix
jmat = asset.joint_probability_matrix(pmat,
filter_shape=(fl, fw),
def cross_correlation_histogram(
binned_spiketrain_i, binned_spiketrain_j, window='full',
border_correction=False, binary=False, kernel=None, method='speed',
cross_correlation_coefficient=False):
"""
Computes the cross-correlation histogram (CCH) between two binned spike
trains `binned_spiketrain_i` and `binned_spiketrain_j`.
Visualization of this function is covered in Viziphant:
:func:`viziphant.spike_train_correlation.plot_cross_correlation_histogram`.
Parameters
----------
binned_spiketrain_i, binned_spiketrain_j :
elephant.conversion.BinnedSpikeTrain
Binned spike trains of lengths N and M to cross-correlate. The input
spike trains can have any `t_start` and `t_stop`.
window : {'valid', 'full'} or list of int, optional
‘full’: This returns the cross-correlation at each point of overlap,
with an output shape of (N+M-1,). At the end-points of the
cross-correlogram, the signals do not overlap completely, and
boundary effects may be seen.
‘valid’: Mode valid returns output of length max(M, N) - min(M, N) + 1.
The cross-correlation product is only given for points where
the signals overlap completely.
Values outside the signal boundary have no effect.
List of integers (min_lag, max_lag):
The entries of window are two integers representing the left and
right extremes (expressed as number of bins) where the
cross-correlation is computed.
Default: 'full'.
border_correction : bool, optional
whether to correct for the border effect. If True, the value of the
CCH at bin :math:`b` (for :math:`b=-H,-H+1, ...,H`, where :math:`H` is
the CCH half-length) is multiplied by the correction factor:
.. math::
(H+1)/(H+1-|b|),
which linearly corrects for loss of bins at the edges.
Default: False.
binary : bool, optional
If True, spikes falling in the same bin are counted as a single spike;
otherwise they are counted as different spikes.
Default: False.
kernel : np.ndarray or None, optional
A one dimensional array containing a smoothing kernel applied
to the resulting CCH. The length N of the kernel indicates the
smoothing window. The smoothing window cannot be larger than the
maximum lag of the CCH. The kernel is normalized to unit area before
being applied to the resulting CCH. Popular choices for the kernel are
* normalized boxcar kernel: `numpy.ones(N)`
* hamming: `numpy.hamming(N)`
* hanning: `numpy.hanning(N)`
* bartlett: `numpy.bartlett(N)`
If None, the CCH is not smoothed.
Default: None.
method : {'speed', 'memory'}, optional
Defines the algorithm to use. "speed" uses `numpy.correlate` to
calculate the correlation between two binned spike trains using a
non-sparse data representation. Due to various optimizations, it is the
fastest realization. In contrast, the option "memory" uses an own
implementation to calculate the correlation based on sparse matrices,
which is more memory efficient but slower than the "speed" option.
Default: "speed".
cross_correlation_coefficient : bool, optional
If True, a normalization is applied to the CCH to obtain the
cross-correlation coefficient function ranging from -1 to 1 according
to Equation (5.10) in [1]_. See Notes.
Default: False.
Returns
-------
cch_result : neo.AnalogSignal
Containing the cross-correlation histogram between
`binned_spiketrain_i` and `binned_spiketrain_j`.
Offset bins correspond to correlations at delays equivalent
to the differences between the spike times of `binned_spiketrain_i` and
those of `binned_spiketrain_j`: an entry at positive lag corresponds to
a spike in `binned_spiketrain_j` following a spike in
`binned_spiketrain_i` bins to the right, and an entry at negative lag
corresponds to a spike in `binned_spiketrain_i` following a spike in
`binned_spiketrain_j`.
To illustrate this definition, consider two spike trains with the same
`t_start` and `t_stop`:
`binned_spiketrain_i` ('reference neuron') : 0 0 0 0 1 0 0 0 0 0 0
`binned_spiketrain_j` ('target neuron') : 0 0 0 0 0 0 0 1 0 0 0
Here, the CCH will have an entry of `1` at `lag=+3`.
Consistent with the definition of `neo.AnalogSignals`, the time axis
represents the left bin borders of each histogram bin. For example,
the time axis might be:
`np.array([-2.5 -1.5 -0.5 0.5 1.5]) * ms`
lags : np.ndarray
Contains the IDs of the individual histogram bins, where the central
bin has ID 0, bins to the left have negative IDs and bins to the right
have positive IDs, e.g.,:
`np.array([-3, -2, -1, 0, 1, 2, 3])`
Notes
-----
1. The Eq. (5.10) in [1]_ is valid for binned spike trains with at most one
spike per bin. For a general case, refer to the implementation of
`_covariance_sparse()`.
2. Alias: `cch`
References
----------
.. [1] "Analysis of parallel spike trains", 2010, Gruen & Rotter, Vol 7.
Examples
--------
Plot the cross-correlation histogram between two Poisson spike trains
>>> import elephant
>>> import matplotlib.pyplot as plt
>>> import quantities as pq
>>> binned_spiketrain_i = elephant.conversion.BinnedSpikeTrain(
... elephant.spike_train_generation.homogeneous_poisson_process(
... 10. * pq.Hz, t_start=0 * pq.ms, t_stop=5000 * pq.ms),
... bin_size=5. * pq.ms)
>>> binned_spiketrain_j = elephant.conversion.BinnedSpikeTrain(
... elephant.spike_train_generation.homogeneous_poisson_process(
... 10. * pq.Hz, t_start=0 * pq.ms, t_stop=5000 * pq.ms),
... bin_size=5. * pq.ms)
>>> cc_hist = \
... elephant.spike_train_correlation.cross_correlation_histogram(
... binned_spiketrain_i, binned_spiketrain_j, window=[-30,30],
... border_correction=False,
... binary=False, kernel=None, method='memory')
>>> plt.bar(left=cc_hist[0].times.magnitude,
... height=cc_hist[0][:, 0].magnitude,
... width=cc_hist[0].sampling_period.magnitude)
>>> plt.xlabel('time (' + str(cc_hist[0].times.units) + ')')
>>> plt.ylabel('cross-correlation histogram')
>>> plt.axis('tight')
>>> plt.show()
"""
# Check that the spike trains are binned with the same temporal
# resolution
if binned_spiketrain_i.shape[0] != 1 or \
binned_spiketrain_j.shape[0] != 1:
raise ValueError("Spike trains must be one dimensional")
# rescale to the common units
# this does not change the data - only its representation
binned_spiketrain_j.rescale(binned_spiketrain_i.units)
if not np.isclose(binned_spiketrain_i._bin_size,
binned_spiketrain_j._bin_size):
raise ValueError("Bin sizes must be equal")
bin_size = binned_spiketrain_i._bin_size
left_edge_min = -binned_spiketrain_i.n_bins + 1
right_edge_max = binned_spiketrain_j.n_bins - 1
t_lags_shift = (binned_spiketrain_j._t_start -
binned_spiketrain_i._t_start) / bin_size
if not np.isclose(t_lags_shift, round(t_lags_shift)):
# For example, if bin_size=1 ms, binned_spiketrain_i.t_start=0 ms, and
# binned_spiketrain_j.t_start=0.5 ms then there is a global shift in
# the binning of the spike trains.
raise ValueError(
"Binned spiketrains time shift is not multiple of bin_size")
t_lags_shift = int(round(t_lags_shift))
# In the examples below we fix st2 and "move" st1.
# Zero-lag is equal to `max(st1.t_start, st2.t_start)`.
# Binned spiketrains (t_start and t_stop) with bin_size=1ms:
# 1) st1=[3, 8] ms, st2=[1, 13] ms
# t_start_shift = -2 ms
# zero-lag is at 3 ms
# 2) st1=[1, 7] ms, st2=[2, 9] ms
# t_start_shift = 1 ms
# zero-lag is at 2 ms
# 3) st1=[1, 7] ms, st2=[4, 6] ms
# t_start_shift = 3 ms
# zero-lag is at 4 ms
# Find left and right edges of unaligned (time-dropped) time signals
if len(window) == 2 and np.issubdtype(type(window[0]), np.integer) \
and np.issubdtype(type(window[1]), np.integer):
# ex. 1) lags range: [w[0] - 2, w[1] - 2] ms
# ex. 2) lags range: [w[0] + 1, w[1] + 1] ms
# ex. 3) lags range: [w[0] + 3, w[0] + 3] ms
if window[0] >= window[1]:
raise ValueError(
"Window's left edge ({left}) must be lower than the right "
"edge ({right})".format(left=window[0], right=window[1]))
left_edge, right_edge = np.subtract(window, t_lags_shift)
if left_edge < left_edge_min or right_edge > right_edge_max:
raise ValueError(
"The window exceeds the length of the spike trains")
lags = np.arange(window[0], window[1] + 1, dtype=np.int32)
cch_mode = 'pad'
elif window == 'full':
# cch computed for all the possible entries
# ex. 1) lags range: [-6, 9] ms
# ex. 2) lags range: [-4, 7] ms
# ex. 3) lags range: [-2, 4] ms
left_edge = left_edge_min
right_edge = right_edge_max
lags = np.arange(left_edge + t_lags_shift,
right_edge + 1 + t_lags_shift, dtype=np.int32)
cch_mode = window
elif window == 'valid':
lags = _CrossCorrHist.get_valid_lags(binned_spiketrain_i,
binned_spiketrain_j)
left_edge, right_edge = lags[(0, -1), ]
cch_mode = window
else:
raise ValueError("Invalid window parameter")
if binary:
binned_spiketrain_i = binned_spiketrain_i.binarize()
binned_spiketrain_j = binned_spiketrain_j.binarize()
cch_builder = _CrossCorrHist(binned_spiketrain_i, binned_spiketrain_j,
window=(left_edge, right_edge))
if method == 'memory':
cross_corr = cch_builder.correlate_memory(cch_mode=cch_mode)
else:
cross_corr = cch_builder.correlate_speed(cch_mode=cch_mode)
if border_correction:
if window == 'valid':
warnings.warn(
"Border correction does not have any effect in "
"'valid' window mode since there are no border effects!")
else:
cross_corr = cch_builder.border_correction(cross_corr)
if kernel is not None:
cross_corr = cch_builder.kernel_smoothing(cross_corr, kernel=kernel)
if cross_correlation_coefficient:
cross_corr = cch_builder.cross_correlation_coefficient(cross_corr)
normalization = 'normalized' if cross_correlation_coefficient else 'counts'
annotations = dict(window=window, border_correction=border_correction,
binary=binary, kernel=kernel is not None,
normalization=normalization)
annotations = dict(cch_parameters=annotations)
# Transform the array count into an AnalogSignal
t_start = pq.Quantity((lags[0] - 0.5) * bin_size,
units=binned_spiketrain_i.units, copy=False)
cch_result = neo.AnalogSignal(
signal=np.expand_dims(cross_corr, axis=1),
units=pq.dimensionless,
t_start=t_start,
sampling_period=binned_spiketrain_i.bin_size, copy=False,
**annotations)
return cch_result, lags
def generate_sts(data_type, N=100):
'''
Generate a list of parallel spike trains with different statistics.
The data are composed of background spiking activity plus possibly
a repeated sequence of synchronous events (SSE).
The background activity depends on the value of data_type.
The size and occurrence count of the SSE is specified by sse_params.
Parameters
----------
data_type : int
An integer specifying the type of background activity.
At the moment the following types of background activity are
supported (note: homog = across neurons; stat = over time):
0 : 100 indep Poisson with rate 25 Hz
1: 100 indep Poisson nonstat-step (10/60/10 Hz)
2: 100 indep Poisson heterog (5->25 Hz), stat
3 : 100 indep Poisson, rate increase with latency variability
N: int
total number of neurons in the model. The default is N=100.
Output
------
sts : list of SpikeTrains
a list of spike trains
params : dict
a dictionary of simulation parameters
'''
T = 1 * pq.s # simulation time
sampl_period = 10 * pq.ms # sampling period of the rate profile
params = {'nr_neurons': N, 'simul_time': T}
# Indep Poisson homog, stat rate 25 Hz
if data_type == 0:
# Define a rate profile
rate = 25 * pq.Hz
# Generate data
sts = stg._n_poisson(rate=rate, t_stop=T, n=N)
# Storing rate parameter
params['rate'] = rate
# Indep Poisson, homog, nonstat-step (10/60/10 Hz)
elif data_type == 1:
a0, a1 = 10 * pq.Hz, 60 * pq.Hz # baseline and transient rates
t1, t2 = 600 * pq.ms, 700 * pq.ms # time segment of transient rate
# Define a rate profile
times = sampl_period.units * np.arange(
0,
T.rescale(sampl_period.units).magnitude, sampl_period.magnitude)
rate_profile = np.zeros(times.shape)
rate_profile[np.any([times < t1, times > t2], axis=0)] = a0.magnitude
rate_profile[np.all([times >= t1, times <= t2], axis=0)] = a1.magnitude
rate_profile = rate_profile * a0.units
rate_profile = neo.AnalogSignal(rate_profile,
sampling_period=sampl_period)
# Generate data
sts = [
stg.inhomogeneous_poisson_process(rate_profile) for i in range(N)
]
# Storing rate parameter
params['rate'] = rate_profile
# Indep Poisson, heterog (5->15 Hz), stat
elif data_type == 2:
rate_min = 5 * pq.Hz # min rate. Ensures that there is >=1 spike
rate_max = 25 * pq.Hz # max rate
rates = np.linspace(rate_min.magnitude, rate_max.magnitude, N) * pq.Hz
# Define a rate profile
# Generate data
sts = [
stg.homogeneous_poisson_process(rate=rate, t_stop=T)
for rate in rates
]
random.shuffle(sts)
# Storing rate parameter
params['rate'] = rates
# Indep Poisson, rate increase sequence
elif data_type == 3:
l = 20 # 20 groups of neurons
w = 5 # of 5 neurons each
t0 = 50 * pq.ms # the first of which increases the rate at time t0
t00 = 500 * pq.ms # and again at time t00
ratechange_dur = 5 * pq.ms # old: 10ms # for a short period
a0, a1 = 14 * pq.Hz, 100 * pq.Hz # from rate a0 to a1
ratechange_delay = 5 * pq.ms # old: 10ms; followed with delay by next group
# Define a rate profile
times = sampl_period.units * np.arange(
0,
T.rescale(sampl_period.units).magnitude, sampl_period.magnitude)
sts = []
rate_profiles = []
for i in range(l * w):
t1 = t0 + (i // w) * ratechange_delay
t2 = t1 + ratechange_dur
t11 = t00 + (i // w) * ratechange_delay
t22 = t11 + ratechange_dur
rate_profile = np.zeros(times.shape)
rate_profile[np.any([times < t1, times > t2], axis=0)] = \
a0.magnitude
rate_profile[np.all([times >= t1, times <= t2], axis=0)] = \
a1.magnitude
rate_profile[np.all([times >= t11, times <= t22], axis=0)] = \
a1.magnitude
rate_profile = rate_profile * a0.units
rate_profile = neo.AnalogSignal(rate_profile,
sampling_period=sampl_period)
rate_profiles.append(rate_profile)
# Generate data
sts.append(stg.inhomogeneous_poisson_process(rate_profile))
# Storing rate parameter
params['rate'] = rate_profiles
else:
raise ValueError(
'data_type %d not supported. Provide int from 0 to 10' % data_type)
return sts, params
def cross_correlation_histogram(binned_st1,
binned_st2,
window='full',
border_correction=False,
binary=False,
kernel=None,
method='speed',
cross_corr_coef=False):
"""
Computes the cross-correlation histogram (CCH) between two binned spike
trains `binned_st1` and `binned_st2`.
Parameters
----------
binned_st1, binned_st2 : elephant.conversion.BinnedSpikeTrain
Binned spike trains to cross-correlate. The two spike trains must have
same `t_start` and `t_stop`.
window : {'valid', 'full', list}, optional
String or list of integers.
‘full’: This returns the cross-correlation at each point of overlap,
with an output shape of (N+M-1,). At the end-points of the
cross-correlogram, the signals do not overlap completely, and
boundary effects may be seen.
‘valid’: Mode valid returns output of length max(M, N) - min(M, N) + 1.
The cross-correlation product is only given for points where
the signals overlap completely.
Values outside the signal boundary have no effect.
List of integers (min_lag, max_lag):
The entries of window are two integers representing the left and
right extremes (expressed as number of bins) where the
cross-correlation is computed.
Default: 'full'
border_correction : bool, optional
whether to correct for the border effect. If True, the value of the
CCH at bin b (for b=-H,-H+1, ...,H, where H is the CCH half-length)
is multiplied by the correction factor:
(H+1)/(H+1-|b|),
which linearly corrects for loss of bins at the edges.
Default: False
binary : bool, optional
whether to binary spikes from the same spike train falling in the
same bin. If True, such spikes are considered as a single spike;
otherwise they are considered as different spikes.
Default: False.
kernel : array or None, optional
A one dimensional array containing an optional smoothing kernel applied
to the resulting CCH. The length N of the kernel indicates the
smoothing window. The smoothing window cannot be larger than the
maximum lag of the CCH. The kernel is normalized to unit area before
being applied to the resulting CCH. Popular choices for the kernel are
* normalized boxcar kernel: numpy.ones(N)
* hamming: numpy.hamming(N)
* hanning: numpy.hanning(N)
* bartlett: numpy.bartlett(N)
If None is specified, the CCH is not smoothed.
Default: None
method : string, optional
Defines the algorithm to use. "speed" uses numpy.correlate to calculate
the correlation between two binned spike trains using a non-sparse data
representation. Due to various optimizations, it is the fastest
realization. In contrast, the option "memory" uses an own
implementation to calculate the correlation based on sparse matrices,
which is more memory efficient but slower than the "speed" option.
Default: "speed"
cross_corr_coef : bool, optional
Normalizes the CCH to obtain the cross-correlation coefficient
function ranging from -1 to 1 according to Equation (5.10) in [1]_.
See Notes.
Returns
-------
cch : neo.AnalogSignal
Containing the cross-correlation histogram between `binned_st1` and
`binned_st2`.
The central bin of the histogram represents correlation at zero
delay (instantaneous correlation).
Offset bins correspond to correlations at a delay equivalent
to the difference between the spike times of `binned_st1` and those of
`binned_st2`: an entry at positive lags corresponds to a spike in
`binned_st2` following a spike in `binned_st1` bins to the right, and
an entry at negative lags corresponds to a spike in `binned_st1`
following a spike in `binned_st2`.
To illustrate this definition, consider the two spike trains:
`binned_st1`: 0 0 0 0 1 0 0 0 0 0 0
`binned_st2`: 0 0 0 0 0 0 0 1 0 0 0
Here, the CCH will have an entry of 1 at lag h=+3.
Consistent with the definition of AnalogSignals, the time axis
represents the left bin borders of each histogram bin. For example,
the time axis might be:
`np.array([-2.5 -1.5 -0.5 0.5 1.5]) * ms`
bin_ids : np.ndarray
Contains the IDs of the individual histogram bins, where the central
bin has ID 0, bins the left have negative IDs and bins to the right
have positive IDs, e.g.,:
`np.array([-3, -2, -1, 0, 1, 2, 3])`
Notes
-----
The Eq. (5.10) in [1]_ is valid for binned spike trains with at most one
spike per bin. For a general case, refer to the implementation of
`_covariance_sparse()`.
References
----------
.. [1] "Analysis of parallel spike trains", 2010, Gruen & Rotter, Vol 7.
Example
-------
Plot the cross-correlation histogram between two Poisson spike trains
>>> import elephant
>>> import matplotlib.pyplot as plt
>>> import quantities as pq
>>> binned_st1 = elephant.conversion.BinnedSpikeTrain(
>>> elephant.spike_train_generation.homogeneous_poisson_process(
>>> 10. * pq.Hz, t_start=0 * pq.ms, t_stop=5000 * pq.ms),
>>> binsize=5. * pq.ms)
>>> binned_st2 = elephant.conversion.BinnedSpikeTrain(
>>> elephant.spike_train_generation.homogeneous_poisson_process(
>>> 10. * pq.Hz, t_start=0 * pq.ms, t_stop=5000 * pq.ms),
>>> binsize=5. * pq.ms)
>>> cc_hist = \
>>> elephant.spike_train_correlation.cross_correlation_histogram(
>>> binned_st1, binned_st2, window=[-30,30],
>>> border_correction=False,
>>> binary=False, kernel=None, method='memory')
>>> plt.bar(left=cc_hist[0].times.magnitude,
>>> height=cc_hist[0][:, 0].magnitude,
>>> width=cc_hist[0].sampling_period.magnitude)
>>> plt.xlabel('time (' + str(cc_hist[0].times.units) + ')')
>>> plt.ylabel('cross-correlation histogram')
>>> plt.axis('tight')
>>> plt.show()
Alias
-----
`cch`
"""
# Check that the spike trains are binned with the same temporal
# resolution
if not binned_st1.matrix_rows == 1:
raise ValueError("Spike train must be one dimensional")
if not binned_st2.matrix_rows == 1:
raise ValueError("Spike train must be one dimensional")
if not np.isclose(binned_st1.binsize.simplified.magnitude,
binned_st2.binsize.simplified.magnitude):
raise ValueError("Bin sizes must be equal")
# Check t_start and t_stop identical (to drop once that the
# pad functionality wil be available in the BinnedSpikeTrain class)
if not binned_st1.t_start == binned_st2.t_start:
raise ValueError("Spike train must have same t start")
if not binned_st1.t_stop == binned_st2.t_stop:
raise ValueError("Spike train must have same t stop")
# The maximum number of of bins
max_num_bins = max(binned_st1.num_bins, binned_st2.num_bins)
# Set the time window in which is computed the cch
# Window parameter given in number of bins (integer)
if isinstance(window[0], int) and isinstance(window[1], int):
# Check the window parameter values
if window[0] >= window[1] or window[0] <= -max_num_bins \
or window[1] >= max_num_bins:
raise ValueError(
"The window exceeds the length of the spike trains")
# Assign left and right edges of the cch
left_edge, right_edge = window[0], window[1]
# The mode in which to compute the cch for the speed implementation
cch_mode = 'pad'
# Case without explicit window parameter
elif window == 'full':
# cch computed for all the possible entries
# Assign left and right edges of the cch
right_edge = binned_st2.num_bins - 1
left_edge = -binned_st1.num_bins + 1
cch_mode = window
# cch compute only for the entries that completely overlap
elif window == 'valid':
# cch computed only for valid entries
# Assign left and right edges of the cch
right_edge = max(binned_st2.num_bins - binned_st1.num_bins, 0)
left_edge = min(binned_st2.num_bins - binned_st1.num_bins, 0)
cch_mode = window
# Check the mode parameter
else:
raise ValueError("Invalid window parameter")
if binary:
binned_st1 = binned_st1.binarize(copy=True)
binned_st2 = binned_st2.binarize(copy=True)
cch_builder = CrossCorrHist(binned_st1,
binned_st2,
window=(left_edge, right_edge))
if method == 'memory':
cross_corr = cch_builder.correlate_memory()
else:
cross_corr = cch_builder.correlate_speed(cch_mode=cch_mode)
bin_ids = np.arange(left_edge, right_edge + 1)
if border_correction:
cross_corr = cch_builder.border_correction(cross_corr)
if kernel is not None:
cross_corr = cch_builder.kernel_smoothing(cross_corr, kernel=kernel)
if cross_corr_coef:
cross_corr = cch_builder.cross_corr_coef(cross_corr)
# Transform the array count into an AnalogSignal
cch_result = neo.AnalogSignal(
signal=cross_corr.reshape(cross_corr.size, 1),
units=pq.dimensionless,
t_start=(bin_ids[0] - 0.5) * binned_st1.binsize,
sampling_period=binned_st1.binsize)
# Return only the hist_bins bins and counts before and after the
# central one
return cch_result, bin_ids
import quantities as pq
import numpy as np
import nixio as nix
from neo.io import NixIO
block = neo.Block()
chn_index = neo.ChannelIndex([0, 1, 2],
channel_names=["a", "b", "c"],
channel_ids=[1, 2, 3])
block.channel_indexes.append(chn_index)
unit = neo.Unit(name="x", description="contain1st")
chn_index.units.append(unit)
seg = neo.Segment()
asig = neo.AnalogSignal(name="signal",
signal=[1.1, 1.2, 1.5],
units="mV",
sampling_rate=1 * pq.Hz)
seg.analogsignals.append(asig)
asig2 = neo.AnalogSignal(name="signal2",
signal=[1.1, 1.2, 2.5],
units="mV",
sampling_rate=1 * pq.Hz)
seg.analogsignals.append(asig2)
irasig = neo.IrregularlySampledSignal(name="irsignal",
signal=np.random.random((100, 2)),
units="mV",
times=np.cumsum(
np.random.random(100) * pq.s))
seg.irregularlysampledsignals.append(irasig)
event = neo.Event(name="event",
times=np.cumsum(np.random.random(10)) * pq.ms,
def get_weights(self):
signal = neo.AnalogSignal(self._weights, units='nA', sampling_period=self.interval * ms,
name="weight")
signal.channel_index = neo.ChannelIndex(numpy.arange(len(self._weights[0])))
return signal
