pybaselines.Baseline2D.pspline_brpls

Baseline2D.pspline_brpls(data, lam=1000.0, num_knots=25, spline_degree=3, diff_order=2, max_iter=50, tol=0.001, max_iter_2=50, tol_2=0.001, weights=None)[source]

A penalized spline version of the brPLS algorithm.

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 1e3.
num_knotsint or sequence[int, int], optional: The number of knots for the splines along the rows and columns, respectively. If a single value is given, both will use the same value. Default is 25.
spline_degreeint or sequence[int, int], optional: The degree of the splines along the rows and columns, respectively. If a single value is given, both will use the same value. Default is 3, which is a cubic spline.
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 0. Default is 2 (second order differential matrix). Typical values are 1 or 2.
max_iterint, optional: The max number of fit iterations. Default is 50.
tolfloat, optional: The exit criteria. Default is 1e-3.
max_iter_2float, optional: The number of iterations for updating the proportion of data occupied by peaks. Default is 50.
tol_2float, optional: The exit criteria for the difference between the calculated proportion of data occupied by peaks. Default is 1e-3.
weightsarray_like, shape (M, 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.

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, shape (J, K)
An array containing the calculated tolerance values for each iteration of both threshold values and fit values. Index 0 are the tolerence values for the difference in the peak proportion, and indices >= 1 are the tolerance values for each fit. All values that were not used in fitting have values of 0. Shape J is 2 plus the number of iterations for the threshold to converge (related to max_iter_2, tol_2), and shape K is the maximum of the number of iterations for the threshold and the maximum number of iterations for all of the fits of the various threshold values (related to max_iter and tol).