pybaselines.Baseline.swima

Baseline.swima(data, min_half_window=3, max_half_window=None, smooth_half_window=None, pad_kwargs=None, **kwargs)

Small-window moving average (SWiMA) baseline.

Computes an iterative moving average to smooth peaks and obtain the baseline.

Parameters:

dataarray_like, shape (N,)

The y-values of the measured data, with N data points.

min_half_windowint, optional

The minimum half window value that must be reached before the exit criteria is considered. Can be increased to reduce the calculation time. Default is 3.

max_half_windowint, optional

The maximum number of iterations. Default is None, which will use (N - 1) / 2. Typically does not need to be specified.

smooth_half_windowint, optional

The half window to use for smoothing the input data with a moving average. Default is None, which will use N / 50. Use a value of 0 or less to not smooth the data. See Notes below for more details.

pad_kwargsdict, optional

A dictionary of keyword arguments to pass to pad_edges() for padding the edges of the data to prevent edge effects from smoothing. Default is None.

**kwargs

Deprecated since version 1.2.0: Passing additional keyword arguments is deprecated and will be removed in version 1.4.0. Pass keyword arguments using pad_kwargs.

Returns:

baselinenumpy.ndarray, shape (N,)

The calculated baseline.

dict

A dictionary with the following items:

• 'half_window': list(int)

  A list of the half windows at which the exit criteria was reached. Has a length of 1 if the main exit criteria was intially reached, otherwise has a length of 2.

• 'converged': list(bool or None)

  A list of the convergence status. Has a length of 1 if the main exit criteria was intially reached, otherwise has a length of 2. Each convergence status is True if the main exit criteria was reached, False if the second exit criteria was reached, and None if max_half_window is reached before either exit criteria.

Notes

This algorithm requires the input data to be fairly smooth (noise-free), so it is recommended to either smooth the data beforehand, or specify a smooth_half_window value. Non-smooth data can cause the exit criteria to be reached prematurely (can be avoided by setting a larger min_half_window), while over-smoothed data can cause the exit criteria to be reached later than optimal.

The half-window at which convergence occurs is roughly close to the index-based full-width-at-half-maximum of a peak or feature, but can vary. Therfore, it is better to set a min_half_window that is smaller than expected to not miss the exit criteria.

If the main exit criteria is not reached on the initial fit, a gaussian baseline (which is well handled by this algorithm) is added to the data, and it is re-fit.

References

Schulze, H., et al. A Small-Window Moving Average-Based Fully Automated Baseline Estimation Method for Raman Spectra. Applied Spectroscopy, 2012, 66(7), 757-764.