pynapple.core.time_series.TsdFrame.smooth#

TsdFrame.smooth(std, windowsize=None, time_units='s', size_factor=100, norm=True)#

Smooth a time series with a gaussian kernel.

std is the standard deviation of the gaussian kernel in units of time. If only std is passed, the function will compute the standard deviation and size in number of time points automatically based on the sampling rate of the time series. For example, if the time series tsd has a sample rate of 100 Hz and std is 50 ms, the standard deviation will be converted to an integer through tsd.rate * std = int(100 * 0.05) = 5.

If windowsize is None, the function will select a kernel size as 100 times the std in number of time points. This behavior can be controlled with the parameter size_factor.

norm set to True normalizes the gaussian kernel to sum to 1.

In the following example, a time series tsd with a sampling rate of 100 Hz is convolved with a gaussian kernel. The standard deviation is 0.05 second and the windowsize is 2 second. When instantiating the gaussian kernel from scipy, it corresponds to parameters M = 200 and std=5

>>> tsd.smooth(std=0.05, windowsize=2, time_units='s', norm=False)

This line is equivalent to :

>>> from scipy.signal.windows import gaussian
>>> kernel = gaussian(M = 200, std=5)
>>> tsd.convolve(window)

It is generally a good idea to visualize the kernel before applying any convolution.

See the scipy documentation for the [gaussian window](https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.windows.gaussian.html)

Parameters:

  • std (Number) – Standard deviation in units of time

  • windowsize (Number) – Size of the gaussian window in units of time.

  • time_units (str, optional) – The time units in which std and windowsize are specified (‘us’, ‘ms’, ‘s’ [default]).

  • size_factor (int, optional) – How long should be the kernel size as a function of the standard deviation. Default is 100. Bypassed if windowsize is used.

  • norm (bool, optional) – Whether to normalized the gaussian kernel or not. Default is True.

Returns:

Time series convolved with a gaussian kernel

Return type:

Tsd, TsdFrame, TsdTensor