pybaselines.Baseline2D.drpls

Baseline2D.drpls(data, lam=100000.0, eta=0.5, max_iter=50, tol=0.001, weights=None, diff_order=2)

Doubly reweighted penalized least squares (drPLS) baseline.

Parameters:

dataarray_like, shape (M, N)

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

lamfloat or sequence[float, float], optional

The smoothing parameter for the rows and columns, respectively. If a single value is given, both will use the same value. Larger values will create smoother baselines. Default is 1e5.

etafloat

A term for controlling the value of lam; should be between 0 and 1. Low values will produce smoother baselines, while higher values will more aggressively fit peaks. Default is 0.5.

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.

diff_orderint or sequence[int, int], optional

The order of the differential matrix for the rows and columns, respectively. If a single value is given, both will use the same value. Must be greater than 1. Default is 2 (second order differential matrix). Typical values are 2 or 3.

Returns:

baselinenumpy.ndarray, shape (M, N)

The calculated baseline.

paramsdict

A dictionary with the following items:

• 'weights': numpy.ndarray, shape (M, 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 eta is not between 0 and 1 or if diff_order is less than 2.

References

Xu, D. et al. Baseline correction method based on doubly reweighted penalized least squares, Applied Optics, 2019, 58, 3913-3920.