def read_analogsignal(fh, block_id, array_id):
nix_block = fh.handle.blocks[block_id]
nix_da = nix_block.data_arrays[array_id]
params = {
'name': Reader.Help.get_obj_neo_name(nix_da),
'signal': nix_da[:], # TODO think about lazy data loading
'units': nix_da.unit,
'dtype': nix_da.dtype,
}
s_dim = nix_da.dimensions[0]
sampling = s_dim.sampling_interval * getattr(pq, s_dim.unit)
if 'hz' in s_dim.unit.lower():
params['sampling_rate'] = sampling
else:
params['sampling_period'] = sampling
signal = AnalogSignal(**params)
signal.t_start = Reader.Help.read_quantity(nix_da.metadata, 't_start')
for key, value in Reader.Help.read_attributes(nix_da.metadata, 'analogsignal').items():
setattr(signal, key, value)
signal.annotations = Reader.Help.read_annotations(nix_da.metadata, 'analogsignal')
return signal
def spike_triggered_average(signal, spiketrains, window):
"""
Calculates the spike-triggered averages of analog signals in a time window
relative to the spike times of a corresponding spiketrain for multiple
signals each. The function receives n analog signals and either one or
n spiketrains. In case it is one spiketrain this one is muliplied n-fold
and used for each of the n analog signals.
Parameters
----------
signal : neo AnalogSignal object
'signal' contains n analog signals.
spiketrains : one SpikeTrain or one numpy ndarray or a list of n of either of these.
'spiketrains' contains the times of the spikes in the spiketrains.
window : tuple of 2 Quantity objects with dimensions of time.
'window' is the start time and the stop time, relative to a spike, of
the time interval for signal averaging.
If the window size is not a multiple of the sampling interval of the
signal the window will be extended to the next multiple.
Returns
-------
result_sta : neo AnalogSignal object
'result_sta' contains the spike-triggered averages of each of the
analog signals with respect to the spikes in the corresponding
spiketrains. The length of 'result_sta' is calculated as the number
of bins from the given start and stop time of the averaging interval
and the sampling rate of the analog signal. If for an analog signal
no spike was either given or all given spikes had to be ignored
because of a too large averaging interval, the corresponding returned
analog signal has all entries as nan. The number of used spikes and
unused spikes for each analog signal are returned as annotations to
the returned AnalogSignal object.
Examples
--------
>>> signal = neo.AnalogSignal(np.array([signal1, signal2]).T, units='mV',
... sampling_rate=10/ms)
>>> stavg = spike_triggered_average(signal, [spiketrain1, spiketrain2],
... (-5 * ms, 10 * ms))
"""
# checking compatibility of data and data types
# window_starttime: time to specify the start time of the averaging
# interval relative to a spike
# window_stoptime: time to specify the stop time of the averaging
# interval relative to a spike
window_starttime, window_stoptime = window
if not (isinstance(window_starttime, pq.quantity.Quantity)
and window_starttime.dimensionality.simplified == pq.Quantity(
1, "s").dimensionality):
raise TypeError("The start time of the window (window[0]) "
"must be a time quantity.")
if not (isinstance(window_stoptime, pq.quantity.Quantity)
and window_stoptime.dimensionality.simplified == pq.Quantity(
1, "s").dimensionality):
raise TypeError("The stop time of the window (window[1]) "
"must be a time quantity.")
if window_stoptime <= window_starttime:
raise ValueError(
"The start time of the window (window[0]) must be "
"earlier than the stop time of the window (window[1]).")
# checks on signal
if not isinstance(signal, AnalogSignal):
raise TypeError("Signal must be an AnalogSignal, not %s." %
type(signal))
if len(signal.shape) > 1:
# num_signals: number of analog signals
num_signals = signal.shape[1]
else:
raise ValueError("Empty analog signal, hence no averaging possible.")
if window_stoptime - window_starttime > signal.t_stop - signal.t_start:
raise ValueError("The chosen time window is larger than the "
"time duration of the signal.")
# spiketrains type check
if isinstance(spiketrains, (np.ndarray, SpikeTrain)):
spiketrains = [spiketrains]
elif isinstance(spiketrains, list):
for st in spiketrains:
if not isinstance(st, (np.ndarray, SpikeTrain)):
raise TypeError(
"spiketrains must be a SpikeTrain, a numpy ndarray, or a "
"list of one of those, not %s." % type(spiketrains))
else:
raise TypeError(
"spiketrains must be a SpikeTrain, a numpy ndarray, or a list of "
"one of those, not %s." % type(spiketrains))
# multiplying spiketrain in case only a single spiketrain is given
if len(spiketrains) == 1 and num_signals != 1:
template = spiketrains[0]
spiketrains = []
for i in range(num_signals):
spiketrains.append(template)
# checking for matching numbers of signals and spiketrains
if num_signals != len(spiketrains):
raise ValueError(
"The number of signals and spiketrains has to be the same.")
# checking the times of signal and spiketrains
for i in range(num_signals):
if spiketrains[i].t_start < signal.t_start:
raise ValueError(
"The spiketrain indexed by %i starts earlier than "
"the analog signal." % i)
if spiketrains[i].t_stop > signal.t_stop:
raise ValueError("The spiketrain indexed by %i stops later than "
"the analog signal." % i)
# *** Main algorithm: ***
# window_bins: number of bins of the chosen averaging interval
window_bins = int(
np.ceil(((window_stoptime - window_starttime) *
signal.sampling_rate).simplified))
# result_sta: array containing finally the spike-triggered averaged signal
result_sta = AnalogSignal(np.zeros((window_bins, num_signals)),
sampling_rate=signal.sampling_rate,
units=signal.units)
# setting of correct times of the spike-triggered average
# relative to the spike
result_sta.t_start = window_starttime
used_spikes = np.zeros(num_signals, dtype=int)
unused_spikes = np.zeros(num_signals, dtype=int)
total_used_spikes = 0
for i in range(num_signals):
# summing over all respective signal intervals around spiketimes
for spiketime in spiketrains[i]:
# checks for sufficient signal data around spiketime
if (spiketime + window_starttime >= signal.t_start
and spiketime + window_stoptime <= signal.t_stop):
# calculating the startbin in the analog signal of the
# averaging window for spike
startbin = int(
np.floor(((spiketime + window_starttime - signal.t_start) *
signal.sampling_rate).simplified))
# adds the signal in selected interval relative to the spike
result_sta[:, i] += signal[startbin:startbin + window_bins, i]
# counting of the used spikes
used_spikes[i] += 1
else:
# counting of the unused spikes
unused_spikes[i] += 1
# normalization
result_sta[:, i] = result_sta[:, i] / used_spikes[i]
total_used_spikes += used_spikes[i]
if total_used_spikes == 0:
warnings.warn("No spike at all was either found or used for averaging")
result_sta.annotate(used_spikes=used_spikes, unused_spikes=unused_spikes)
return result_sta
def spike_triggered_average(signal, spiketrains, window):
"""
Calculates the spike-triggered averages of analog signals in a time window
relative to the spike times of a corresponding spiketrain for multiple
signals each. The function receives n analog signals and either one or
n spiketrains. In case it is one spiketrain this one is muliplied n-fold
and used for each of the n analog signals.
Parameters
----------
signal : neo AnalogSignal object
'signal' contains n analog signals.
spiketrains : one SpikeTrain or one numpy ndarray or a list of n of either of these.
'spiketrains' contains the times of the spikes in the spiketrains.
window : tuple of 2 Quantity objects with dimensions of time.
'window' is the start time and the stop time, relative to a spike, of
the time interval for signal averaging.
If the window size is not a multiple of the sampling interval of the
signal the window will be extended to the next multiple.
Returns
-------
result_sta : neo AnalogSignal object
'result_sta' contains the spike-triggered averages of each of the
analog signals with respect to the spikes in the corresponding
spiketrains. The length of 'result_sta' is calculated as the number
of bins from the given start and stop time of the averaging interval
and the sampling rate of the analog signal. If for an analog signal
no spike was either given or all given spikes had to be ignored
because of a too large averaging interval, the corresponding returned
analog signal has all entries as nan. The number of used spikes and
unused spikes for each analog signal are returned as annotations to
the returned AnalogSignal object.
Examples
--------
>>> signal = neo.AnalogSignal(np.array([signal1, signal2]).T, units='mV',
... sampling_rate=10/ms)
>>> stavg = spike_triggered_average(signal, [spiketrain1, spiketrain2],
... (-5 * ms, 10 * ms))
"""
# checking compatibility of data and data types
# window_starttime: time to specify the start time of the averaging
# interval relative to a spike
# window_stoptime: time to specify the stop time of the averaging
# interval relative to a spike
window_starttime, window_stoptime = window
if not (isinstance(window_starttime, pq.quantity.Quantity) and
window_starttime.dimensionality.simplified ==
pq.Quantity(1, "s").dimensionality):
raise TypeError("The start time of the window (window[0]) "
"must be a time quantity.")
if not (isinstance(window_stoptime, pq.quantity.Quantity) and
window_stoptime.dimensionality.simplified ==
pq.Quantity(1, "s").dimensionality):
raise TypeError("The stop time of the window (window[1]) "
"must be a time quantity.")
if window_stoptime <= window_starttime:
raise ValueError("The start time of the window (window[0]) must be "
"earlier than the stop time of the window (window[1]).")
# checks on signal
if not isinstance(signal, AnalogSignal):
raise TypeError(
"Signal must be an AnalogSignal, not %s." % type(signal))
if len(signal.shape) > 1:
# num_signals: number of analog signals
num_signals = signal.shape[1]
else:
raise ValueError("Empty analog signal, hence no averaging possible.")
if window_stoptime - window_starttime > signal.t_stop - signal.t_start:
raise ValueError("The chosen time window is larger than the "
"time duration of the signal.")
# spiketrains type check
if isinstance(spiketrains, (np.ndarray, SpikeTrain)):
spiketrains = [spiketrains]
elif isinstance(spiketrains, list):
for st in spiketrains:
if not isinstance(st, (np.ndarray, SpikeTrain)):
raise TypeError(
"spiketrains must be a SpikeTrain, a numpy ndarray, or a "
"list of one of those, not %s." % type(spiketrains))
else:
raise TypeError(
"spiketrains must be a SpikeTrain, a numpy ndarray, or a list of "
"one of those, not %s." % type(spiketrains))
# multiplying spiketrain in case only a single spiketrain is given
if len(spiketrains) == 1 and num_signals != 1:
template = spiketrains[0]
spiketrains = []
for i in range(num_signals):
spiketrains.append(template)
# checking for matching numbers of signals and spiketrains
if num_signals != len(spiketrains):
raise ValueError(
"The number of signals and spiketrains has to be the same.")
# checking the times of signal and spiketrains
for i in range(num_signals):
if spiketrains[i].t_start < signal.t_start:
raise ValueError(
"The spiketrain indexed by %i starts earlier than "
"the analog signal." % i)
if spiketrains[i].t_stop > signal.t_stop:
raise ValueError(
"The spiketrain indexed by %i stops later than "
"the analog signal." % i)
# *** Main algorithm: ***
# window_bins: number of bins of the chosen averaging interval
window_bins = int(np.ceil(((window_stoptime - window_starttime) *
signal.sampling_rate).simplified))
# result_sta: array containing finally the spike-triggered averaged signal
result_sta = AnalogSignal(np.zeros((window_bins, num_signals)),
sampling_rate=signal.sampling_rate, units=signal.units)
# setting of correct times of the spike-triggered average
# relative to the spike
result_sta.t_start = window_starttime
used_spikes = np.zeros(num_signals, dtype=int)
unused_spikes = np.zeros(num_signals, dtype=int)
total_used_spikes = 0
for i in range(num_signals):
# summing over all respective signal intervals around spiketimes
for spiketime in spiketrains[i]:
# checks for sufficient signal data around spiketime
if (spiketime + window_starttime >= signal.t_start and
spiketime + window_stoptime <= signal.t_stop):
# calculating the startbin in the analog signal of the
# averaging window for spike
startbin = int(np.floor(((spiketime + window_starttime -
signal.t_start) * signal.sampling_rate).simplified))
# adds the signal in selected interval relative to the spike
result_sta[:, i] += signal[
startbin: startbin + window_bins, i]
# counting of the used spikes
used_spikes[i] += 1
else:
# counting of the unused spikes
unused_spikes[i] += 1
# normalization
result_sta[:, i] = result_sta[:, i] / used_spikes[i]
total_used_spikes += used_spikes[i]
if total_used_spikes == 0:
warnings.warn(
"No spike at all was either found or used for averaging")
result_sta.annotate(used_spikes=used_spikes, unused_spikes=unused_spikes)
return result_sta
