timeflux.nodes.dejitter


Dejittering nodes

dejitter

class timeflux.nodes.dejitter.Snap(rate=None)[source]

Bases: timeflux.core.node.Node

Snap time stamps to nearest occurring frequency.

Variables
  • i (Port) – Default input, expects DataFrame and meta.

  • o (Port) – Default output, provides DataArray and meta.

Parameters

rate (float|None) – (optional) nominal sampling frequency of the data, to round the timestamps to (in Hz). If None, the rate will be get from the meta of the input port.

Create instance and initialize the logger.

update(self)[source]
class timeflux.nodes.dejitter.Interpolate(rate=None, method='cubic', n_min=3, n_max=10)[source]

Bases: timeflux.core.node.Node

Dejitter data with values interpolation.

This nodes continuously buffers a small amount of data to allow for interpolating missing samples. The output data is resampled at a fixed rate. The interpolation is performed by Pandas methods.

Variables
  • i (Port) – Default input, expects DataFrame and meta.

  • o (Port) – Default output, provides DataArray and meta.

Parameters
  • rate (float|None) – (optional) nominal sampling frequency of the data. If None,

  • rate will be get from the meta of the input port. (the) –

  • method – interpolation method. See the pandas.DataFrame.interpolate documentation.

  • n_min – minimum number of samples to perform the interpolation.

  • n_max – number of samples to keep in the buffer.

Notes

Computation cost mainly depends on the window size and the estimation is performed in the main thread. Hence, the user should be careful on the computation duration.

Create instance and initialize the logger.

update(self)[source]