pynapple.TsdTensor.to_trial_tensor#

TsdTensor.to_trial_tensor(ep, align='start', padding_value=nan)#

Return trial-based tensor from an IntervalSet object. The shape of the tensor array is (shape of time series, number of trials, number of time points)

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.
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.

Return type:

numpy.ndarray

Examples

>>> import pynapple as nap
>>> import numpy as np
>>> tsdframe = nap.TsdFrame(t=np.arange(100), d=np.arange(200).reshape(2,100).T)
>>> 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 slicing tsdframe for each interval of ep.

>>> tensor = tsdframe.to_trial_tensor(ep)
>>> tensor
array([[[ 20.,  21.,  22.,  nan,  nan,  nan,  nan,  nan,  nan],
        [ 40.,  41.,  42.,  43.,  44.,  nan,  nan,  nan,  nan],
        [ 60.,  61.,  62.,  63.,  64.,  65.,  66.,  nan,  nan],
        [ 80.,  81.,  82.,  83.,  84.,  85.,  86.,  87.,  88.]],
       [[120., 121., 122.,  nan,  nan,  nan,  nan,  nan,  nan],
        [140., 141., 142., 143., 144.,  nan,  nan,  nan,  nan],
        [160., 161., 162., 163., 164., 165., 166.,  nan,  nan],
        [180., 181., 182., 183., 184., 185., 186., 187., 188.]]])

pynapple.TsdTensor.to_trial_tensor#

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