pynapple.core.time_series.TsdFrame.count#
- TsdFrame.count(*args, dtype=None, **kwargs)#
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. tsd.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. tsd.count(1, ep=my_epochs) -> Count occurent of events within a 1 second bin defined on the IntervalSet my_epochs.
3. tsd.count(ep=my_bins) -> Count occurent of events within each epoch of the intervalSet object my_bins
4. tsd.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 Tsd object indexed by the center of the bins.
- Return type:
Examples
This example shows how to count events within bins of 0.1 second.
>>> import pynapple as nap >>> import numpy as np >>> t = np.unique(np.sort(np.random.randint(0, 1000, 100))) >>> ts = nap.Ts(t=t, time_units='s') >>> bincount = ts.count(0.1)
An epoch can be specified:
>>> ep = nap.IntervalSet(start = 100, end = 800, time_units = 's') >>> bincount = ts.count(0.1, ep=ep)
And bincount automatically inherit ep as time support:
>>> bincount.time_support start end 0 100.0 800.0