movement.filtering.savgol_filter#
- movement.filtering.savgol_filter(ds, window_length, polyorder=2, print_report=True, **kwargs)[source]#
Smooth pose tracks by applying a Savitzky-Golay filter over time.
- Parameters:
ds (xarray.Dataset) – Dataset containing position, confidence scores, and metadata.
window_length (int) – The size of the filter window. Window length is interpreted as being in the input dataset’s time unit, which can be inspected with
ds.time_unit
.polyorder (int) – The order of the polynomial used to fit the samples. Must be less than
window_length
. By default, apolyorder
of 2 is used.print_report (bool) – Whether to print a report on the number of NaNs in the dataset before and after filtering. Default is
True
.**kwargs (dict) – Additional keyword arguments are passed to scipy.signal.savgol_filter. Note that the
axis
keyword argument may not be overridden.
- Returns:
ds_smoothed – The provided dataset (ds), where pose tracks have been smoothed using a Savitzky-Golay filter with the provided parameters.
- Return type:
xarray.Dataset
Notes
Uses the
scipy.signal.savgol_filter
function to apply a Savitzky-Golay filter to the input dataset’sposition
variable. See the scipy documentation for more information on that function. Whenever one or more NaNs are present in a filter window of the input dataset, a NaN is returned to the output array. As a result, any stretch of NaNs present in the input dataset will be propagated proportionally to the size of the window in frames (specifically, byfloor(window_length/2)
). Note that, unlikemovement.filtering.median_filter()
, there is nomin_periods
option to control this behaviour.