pybaselines.spline.pspline_psalsa

pybaselines.spline.pspline_psalsa(data, lam=1000.0, p=0.5, k=None, num_knots=100, spline_degree=3, diff_order=2, max_iter=50, tol=0.001, weights=None, x_data=None)[source]

A penalized spline version of the psalsa algorithm.

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 1e3.
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 0.5.
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().
num_knotsint, optional: The number of knots for the spline. Default is 100.
spline_degreeint, optional: The degree of the spline. Default is 3, which is a cubic spline.
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.
x_dataarray_like, shape (N,), optional: The x-values of the measured data. Default is None, which will create an array from -1 to 1 with N points.

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.