interpolate_over_time#

movement.filtering.interpolate_over_time(data, method='linear', max_gap=None, print_report=False, **kwargs)[source]#

Fill in NaN values by interpolating over the time dimension.

This function calls xarray.DataArray.interpolate_na() and can pass additional keyword arguments to it, depending on the chosen method. See the xarray documentation for more details.

Parameters:

data (DataArray) – The input data to be interpolated.
method (Union[Literal['linear', 'nearest', 'zero', 'slinear', 'quadratic', 'cubic', 'quintic', 'polynomial'], Literal['barycentric', 'krogh', 'pchip', 'spline', 'akima', 'makima'], Literal['linear', 'nearest', 'slinear', 'cubic', 'quintic', 'pchip']]) – String indicating which method to use for interpolation. Default is linear.
max_gap (int | None) – Maximum size of gap, a continuous sequence of missing observations (represented as NaNs), to fill. The default value is None (no limit). Gap size is defined as the number of consecutive NaNs (see Notes for more information).
print_report (bool) – Whether to print a report on the number of NaNs in the dataset before and after interpolation. Default is False.
**kwargs (Any) – Any **kwargs accepted by xarray.DataArray.interpolate_na(), which in turn passes them verbatim to the underlying interpolation methods.

Returns:

The data where NaN values have been interpolated over using the parameters provided.

Return type:

DataArray

Notes

The max_gap parameter differs slightly from that in xarray.DataArray.interpolate_na(), in which the gap size is defined as the difference between the time coordinate values at the first data point after a gap and the last value before a gap.