pybaselines.Baseline.derpsalsa

Baseline.derpsalsa(data, lam=1000000.0, p=0.01, k=None, diff_order=2, max_iter=50, tol=0.001, weights=None, smooth_half_window=None, num_smooths=16, pad_kwargs=None, **kwargs)

Derivative Peak-Screening Asymmetric Least Squares Algorithm (derpsalsa).

Parameters:

dataarray_like, shape (N,)

The y-values of the measured data, with N data points. Must not contain missing data (NaN) or Inf.

lamfloat, optional

The smoothing parameter. Larger values will create smoother baselines. Default is 1e6.

pfloat, optional

The penalizing weighting factor. Must be between 0 and 1. Values greater than the baseline will be given p weight, and values less than the baseline will be given 1 - p weight. Default is 1e-2.

kfloat, optional

A factor that controls the exponential decay of the weights for baseline values greater than the data. Should be approximately the height at which a value could be considered a peak. Default is None, which sets k to one-tenth of the standard deviation of the input data. A large k value will produce similar results to asls().

diff_orderint, optional

The order of the differential matrix. Must be greater than 0. Default is 2 (second order differential matrix). Typical values are 2 or 1.

max_iterint, optional

The max number of fit iterations. Default is 50.

tolfloat, optional

The exit criteria. Default is 1e-3.

weightsarray_like, shape (N,), optional

The weighting array. If None (default), then the initial weights will be an array with size equal to N and all values set to 1.

smooth_half_windowint, optional

The half-window to use for smoothing the data before computing the first and second derivatives. Default is None, which will use len(data) / 200.

num_smoothsint, optional

The number of times to smooth the data before computing the first and second derivatives. Default is 16.

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.

paramsdict

A dictionary with the following items:

• 'weights': numpy.ndarray, shape (N,)

  The weight array used for fitting the data.

• 'tol_history': numpy.ndarray

  An array containing the calculated tolerance values for each iteration. The length of the array is the number of iterations completed. If the last value in the array is greater than the input tol value, then the function did not converge.

Raises:

ValueError

Raised if p is not between 0 and 1. Also raised if k is not greater than 0.

References

Korepanov, V. Asymmetric least-squares baseline algorithm with peak screening for automatic processing of the Raman spectra. Journal of Raman Spectroscopy. 2020, 51(10), 2061-2065.