pybaselines.Baseline.snip

Baseline.snip(data, max_half_window=None, decreasing=False, smooth_half_window=None, filter_order=2, pad_kwargs=None, **kwargs)

Statistics-sensitive Non-linear Iterative Peak-clipping (SNIP).

Parameters:

dataarray_like, shape (N,)

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

max_half_windowint or sequence(int, int), optional

The maximum number of iterations. Should be set such that max_half_window is approxiamtely (w-1)/2, where w is the index-based width of a feature or peak. max_half_window can also be a sequence of two integers for asymmetric peaks, with the first item corresponding to the max_half_window of the peak's left edge, and the second item for the peak's right edge [3]. Default is None, which will use the output from optimize_window(), which is an okay starting value.

decreasingbool, optional

If False (default), will iterate through window sizes from 1 to max_half_window. If True, will reverse the order and iterate from max_half_window to 1, which gives a smoother baseline according to [3] and [4].

smooth_half_windowint, optional

The half window to use for smoothing the data. If smooth_half_window is greater than 0, will perform a moving average smooth on the data for each window, which gives better results for noisy data [3]. Default is None, which will not perform any smoothing.

filter_order{2, 4, 6, 8}, optional

If the measured data has a more complicated baseline consisting of other elements such as Compton edges, then a higher filter_order should be selected [3]. Default is 2, which works well for approximating a linear baseline.

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

An empty dictionary, just to match the output of all other algorithms.

Raises:

ValueError

Raised if filter_order is not 2, 4, 6, or 8.

Warns:

UserWarning

Raised if max_half_window is greater than (len(data) - 1) // 2.

Notes

Algorithm initially developed by [1], and this specific version of the algorithm is adapted from [2], [3], and [4].

If data covers several orders of magnitude, better results can be obtained by first transforming the data using log-log-square transform before using SNIP [2]:

transformed_data =  np.log(np.log(np.sqrt(data + 1) + 1) + 1)

and then baseline can then be reverted back to the original scale using inverse:

baseline = -1 + (np.exp(np.exp(snip(transformed_data)) - 1) - 1)**2

References

[1]

Ryan, C.G., et al. SNIP, A Statistics-Sensitive Background Treatment For The Quantitative Analysis Of Pixe Spectra In Geoscience Applications. Nuclear Instruments and Methods in Physics Research B, 1988, 934, 396-402.

[2] (1,2)

Morháč, M., et al. Background elimination methods for multidimensional coincidence γ-ray spectra. Nuclear Instruments and Methods in Physics Research A, 1997, 401, 113-132.

[3] (1,2,3,4,5)

Morháč, M., et al. Peak Clipping Algorithms for Background Estimation in Spectroscopic Data. Applied Spectroscopy, 2008, 62(1), 91-106.

[4] (1,2)

Morháč, M. An algorithm for determination of peak regions and baseline elimination in spectroscopic data. Nuclear Instruments and Methods in Physics Research A, 2009, 60, 478-487.