# yasa.moving_transform

yasa.moving_transform(x, y=None, sf=100, window=0.3, step=0.1, method='corr', interp=False)[source]

Moving transformation of one or two time-series.

Parameters
xarray_like

Single-channel data

yarray_like, optional

Second single-channel data (only used if method in [‘corr’, ‘covar’]).

sffloat

Sampling frequency.

windowint

Window size in seconds.

stepint

Step in seconds. A step of 0.1 second (100 ms) is usually a good default. If step == 0, overlap at every sample (slowest) If step == nperseg, no overlap (fastest) Higher values = higher precision = slower computation.

methodstr

Transformation to use. Available methods are:

'mean' : arithmetic mean of x
'min' : minimum value of x
'max' : maximum value of x
'ptp' : peak-to-peak amplitude of x
'prop_above_zero' : proportion of values of x that are above zero
'rms' : root mean square of x
'slope' : slope of the least-square regression of x (in a.u / sec)
'corr' : Correlation between x and y
'covar' : Covariance between x and y

interpboolean

If True, a cubic interpolation is performed to ensure that the output has the same size as the input.

Returns
tnp.array

Time vector, in seconds, corresponding to the MIDDLE of each epoch.

outnp.array

Transformed signal

Notes

This function was inspired by the transform_signal function of the Wonambi package (https://github.com/wonambi-python/wonambi).