pynapple.TsGroup.count#
- TsGroup.count(bin_size=None, ep=None, time_units='s', dtype=None)[source]#
Count occurences of events within bin_size or within a set of bins defined as an IntervalSet. You can call this function in multiple ways :
1. tsgroup.count(bin_size=1, time_units = ‘ms’) -> Count occurence of events within a 1 ms bin defined on the time support of the object.
2. tsgroup.count(1, ep=my_epochs) -> Count occurent of events within a 1 second bin defined on the IntervalSet my_epochs.
3. tsgroup.count(ep=my_bins) -> Count occurent of events within each epoch of the intervalSet object my_bins
4. tsgroup.count() -> Count occurent of events within each epoch of the time support.
bin_size should be seconds unless specified. If bin_size is used and no epochs is passed, the data will be binned based on the time support of the object.
- Parameters:
bin_size (None or float, optional) – The bin size (default is second)
ep (None or IntervalSet, optional) – IntervalSet to restrict the operation
time_units (str, optional) – Time units of bin size (‘us’, ‘ms’, ‘s’ [default])
dtype (type, optional) – Data type for the count. Default is np.int64.
- Returns:
out – A TsdFrame with the columns being the index of each item in the TsGroup.
- Return type:
Examples
This example shows how to count events within bins of 0.1 second for the first 100 seconds.
>>> import pynapple as nap >>> import numpy as np >>> tmp = {0: nap.Ts(t=np.arange(0, 200), time_units='s'), ... 1: nap.Ts(t=np.arange(0, 200, 0.5), time_units='s'), ... 2: nap.Ts(t=np.arange(0, 300, 0.25), time_units='s')} >>> tsgroup = nap.TsGroup(tmp) >>> ep = nap.IntervalSet(start=0, end=100, time_units='s') >>> bincount = tsgroup.count(1, ep) >>> bincount Time (s) 0 1 2 ---------- --- --- --- 0.5 1 2 4 1.5 1 2 4 2.5 1 2 4 3.5 1 2 4 4.5 1 2 4 5.5 1 2 4 6.5 1 2 4 ... 93.5 1 2 4 94.5 1 2 4 95.5 1 2 4 96.5 1 2 4 97.5 1 2 4 98.5 1 2 4 99.5 1 2 4 dtype: int64, shape: (100, 3)