pybaselines.Baseline.aspls

Baseline.aspls(data, lam=100000.0, diff_order=2, max_iter=100, tol=0.001, weights=None, alpha=None, asymmetric_coef=0.5)

Adaptive smoothness penalized least squares smoothing (asPLS).

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 1e5.

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 100.

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.

alphaarray_like, shape (N,), optional

An array of values that control the local value of lam to better fit peak and non-peak regions. If None (default), then the initial values will be an array with size equal to N and all values set to 1.

asymmetric_coeffloat

The asymmetric coefficient for the weighting. Higher values leads to a steeper weighting curve (ie. more step-like). Default is 0.5.

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.

• 'alpha': numpy.ndarray, shape (N,)

  The array of alpha values used for fitting the data in the final iteration.

• '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 alpha and data do not have the same shape. Also raised if asymmetric_coef is not greater than 0.

Notes

The default asymmetric coefficient (k in the asPLS paper) is 0.5 instead of the 2 listed in the asPLS paper. pybaselines uses the factor of 0.5 since it matches the results in Table 2 and Figure 5 of the asPLS paper closer than the factor of 2 and fits noisy data much better.

References

Zhang, F., et al. Baseline correction for infrared spectra using adaptive smoothness parameter penalized least squares method. Spectroscopy Letters, 2020, 53(3), 222-233.