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SplineSmoothing v1

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Summary

Smooths a set of spectra using a cubic spline. Optionally, this algorithm can also calculate derivatives up to order 2 as a side product

See Also

Fit, SplineInterpolation, SplineBackground

Properties

Name	Direction	Type	Default	Description
InputWorkspace	Input	MatrixWorkspace	Mandatory	The workspace on which to perform the smoothing algorithm.
OutputWorkspace	Output	MatrixWorkspace	Mandatory	The workspace containing the calculated points
OutputWorkspaceDeriv	Output	WorkspaceGroup		The workspace containing the calculated derivatives
DerivOrder	Input	number	0	Order to derivatives to calculate.
Error	Input	number	0.05	The amount of error we wish to tolerate in smoothing
MaxNumberOfBreaks	Input	number	0	To set the positions of the break-points, default 0 equally spaced real values in interval 0.0 - 1.0

Description

The algorithm performs a smoothing of the input data using a cubic spline. The algorithm takes a 2D workspace and generates a spline for each of the spectra to approximate a fit of the data.

Optionally, this algorithm can also calculate the first and second derivatives of each of the interpolated points as a side product. Setting the DerivOrder property to zero will force the algorithm to calculate no derivatives.

The algorithm provides user with an option of including number of breaking points which will enable the algorithm to execute functions with large number of spikes or noise. With the control of number of breaking points, user will be able to execute SplineSmoothing algorithm in small period of time. The lower breaking points number is set the faster algorithm will execute the function but it will not produce high quality smoothing function. By inserting high number of breaking points, a detailed smoothing function can also be produced. By not providing MaxNumberOfBreaks, the algorithm will assume that user would like to execute the maximum number of breaks possible. This comes with a risk of algorithm falling in to a very long loop.

By providing MaxNumberOfBreaks as a parameter, the users are also will be able to successfully and efficiently apply the algorithm to a workspace with multiple spectrums, which would generate an output of workspace with multiple spectrums of SplineSmoothing algorithm applied to it. BSpline can be used to help you understand break-points in further detail.

For Histogram Workspaces

If the input workspace contains histograms, rather than data points, then SplineInterpolation will automatically convert the input to point data. The output returned with be in the same format as the input.

Usage

Example - Smoothing noisy data:

ws = CreateSampleWorkspace("Histogram","Multiple Peaks",
    BankPixelWidth=1, NumBanks=10, Random=True,
    XMax=30, BinWidth=0.3)
wsOut = SplineSmoothing(ws,Error=1)

print("This has created a spline for each spectra in the %s workspace" % wsOut)

try:
    plotSpectrum([ws,wsOut],0)
except NameError:
    # plotSpectrum was not available
    pass

Output:

This has created a spline for each spectra in the wsOut workspace

Categories: AlgorithmIndex | Optimization | CorrectionFunctions\BackgroundCorrections

Source

C++ header: SplineSmoothing.h

C++ source: SplineSmoothing.cpp