pynapple.TsGroup.groupby_apply

TsGroup.groupby_apply(by, func, input_key=None, **func_kwargs)

Apply a function to each group in a grouped pynapple object.

Parameters:

• by (str or list of str) – Metadata name(s) to group by.

• func (function) – Function to apply to each group.

• input_key (str or None, optional) – Input key that the grouped object will be passed as. If None, the grouped object will be passed as the first positional argument.

• **func_kwargs (optional) – Additional keyword arguments to pass to the function. Any required positional arguments that are not the grouped object should be passed as keyword arguments.

Returns:

Dictionary of results from applying the function to each group, where the keys are the group names and the values are the results.

Return type:

dict

Examples

>>> import pynapple as nap
>>> import numpy as np
>>> tmp = {0:nap.Ts(t=np.arange(0,40), time_units='s'),
... 1:nap.Ts(t=np.arange(0,40,0.5), time_units='s'),
... 2:nap.Ts(t=np.arange(0,40,0.25), time_units='s'),
... }
>>> metadata = {"l1": [1, 2, 2], "l2": ["x", "x", "y"]}
>>> tsgroup = nap.TsGroup(tmp,metadata=metadata)
>>> print(tsgroup)
  Index     rate    l1  l2
-------  -------  ----  ----
      0  1.00629     1  x
      1  2.01258     2  x
      2  4.02516     2  y

Apply a custom function:

>>> tsgroup.groupby_apply("l2", lambda x: x.to_tsd().shape[0])
{'x': 120, 'y': 160}

Apply a function with additional arguments:

>>> feature = nap.Tsd(
...     t=np.arange(40),
...     d=np.concatenate([np.zeros(20), np.ones(20)]),
...     time_support=nap.IntervalSet(np.array([[0, 5], [10, 12], [20, 33]])),
... )
>>> tsgroup.groupby_apply("l2", nap.compute_1d_tuning_curves, feature=feature, nb_bins=2)
{'x':          0         1
 0.25  1.15  2.044444
 0.75  1.15  2.217857,
 'y':              2
 0.25  3.833333
 0.75  4.353571}