pybaselines.Baseline2D.iasls

Baseline2D.iasls(data, lam=1000000.0, p=0.01, lam_1=0.0001, max_iter=50, tol=0.001, weights=None, diff_order=2)

Fits the baseline using the improved asymmetric least squares (IAsLS) algorithm.

The algorithm consideres both the first and second derivatives of the residual.

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 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.

lam_1float or sequence[float, float], optional

The smoothing parameter for the rows and columns, respectively, of the first derivative of the residual. Default is 1e-4.

max_iterint, optional

The max number of fit iterations. Default is 50.

tolfloat, optional

The exit criteria. Default is 1e-3.

weightsarray_like, shape (M, N), optional

The weighting array. If None (default), then the initial weights will be set by fitting the data with a second order polynomial.

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 p is not between 0 and 1 or if diff_order is less than 2.

References

He, S., et al. Baseline correction for raman spectra using an improved asymmetric least squares method, Analytical Methods, 2014, 6(12), 4402-4407.