pynapple.TsGroup.trial_count#
- TsGroup.trial_count(ep, bin_size, align='start', padding_value=nan, time_unit='s')[source]#
Return trial-based count tensor from an IntervalSet object. The shape of the tensor array is (number of group elements, number of trials, number of time bins).
The bin_size parameter determines the number of time bins.
The align parameter controls how the time series are aligned. If align=”start”, the time series are aligned to the start of each trial. If align=”end”, the time series are aligned to the end of each trial.
If trials have uneven durations, the returned array is padded. The parameter padding_value determine which value is used to pad the array. Default is NaN.
- Parameters:
ep (IntervalSet) – Epochs holding the trials. Each interval can be of unequal size.
bin_size (Number) – The size of the time bins.
align (str, optional) – How to align the time series (‘start’ [default], ‘end’)
padding_value (Number, optional) – How to pad the array if unequal intervals. Default is np.nan.
time_unit (str, optional) – Time units of the bin_size parameter (‘s’ [default], ‘ms’, ‘us’).
- Return type:
numpy.ndarray
- Raises:
RuntimeError – If time_unit not in [“s”, “ms”, “us”]
Examples
>>> import pynapple as nap >>> import numpy as np >>> group = nap.TsGroup({0:nap.Ts(t=np.arange(0, 100))}) >>> ep = nap.IntervalSet(start=np.arange(20, 100, 20), end=np.arange(20, 100, 20) + np.arange(2, 10, 2)) >>> print(ep) index start end 0 20 22 1 40 44 2 60 66 3 80 88 shape: (4, 2), time unit: sec.
Create a trial-based tensor by counting events within 1 second bin for each interval of ep.
>>> tensor = group.trial_count(ep, bin_size=1) >>> tensor array([[[ 1., 1., nan, nan, nan, nan, nan, nan], [ 1., 1., 1., 1., nan, nan, nan, nan], [ 1., 1., 1., 1., 1., 1., nan, nan], [ 1., 1., 1., 1., 1., 1., 1., 1.]]])
By default, the time series are aligned to the start of the epochs. The parameter align control this behavior.
>>> tensor = group.trial_count(ep, bin_size=1, align="end") >>> tensor array([[[nan, nan, nan, nan, nan, nan, 1., 1.], [nan, nan, nan, nan, 1., 1., 1., 1.], [nan, nan, 1., 1., 1., 1., 1., 1.], [ 1., 1., 1., 1., 1., 1., 1., 1.]]])