Altaxo.Science.Spectroscopy.BaselineEstimation Namespace |
[Missing <summary> documentation for "N:Altaxo.Science.Spectroscopy.BaselineEstimation"]
Classes| | Class | Description |
|---|
 | AirPLS |
Implements the adaptive iteratively reweighted penalized least squares algorithm proposed by Zhang et al [1].
|
| AirPLSSerializationSurrogate0 | |
| AirPLSBase |
Implements the adaptive iteratively reweighted penalized least squares algorithm proposed by Zhang et al [1].
|
| ALS |
Implements the Asymmetric Least Squares method for baseline estimation, proposed by Eilers and Boelens 2005 [1].
|
| ALSSerializationSurrogate0 | |
| ALSBase |
Implements the Asymmetric Least Squares method for baseline estimation, proposed by Eilers and Boelens 2005 [1].
|
| ALSMethodsBase |
Base class of ALS based methods, like AirPLSBase, ALSBase and ArPLSBase.
|
| ArPLS |
Implements the asymmetrically reweighted penalized least squares algorithm proposed by Baek et al [1].
|
| ArPLSSerializationSurrogate0 | |
| ArPLSBase |
Implements the asymmetrically reweighted penalized least squares algorithm proposed by Baek et al [1].
|
| BaselineEstimationNone |
Does nothing (null operation).
|
| BaselineEstimationNoneSerializationSurrogate0 | |
| ISREA |
This class detrends a spectrum. This is done by fitting a smoothing spline through the spectrum.
Then the spectral data that lies above the spline are reduced in amplitude toward the spline.
The process is repeated until the resulting curve changes no more.
|
| ISREASerializationSurrogate0 |
2024-04-16 V0 initial version.
|
| ISREABase |
This class detrends a spectrum. This is done by fitting a smoothing spline through the spectrum.
Then the spectral data that lies above the spline are reduced in amplitude toward the spline.
The process is repeated until the resulting curve changes no more.
|
| PolynomialDetrending |
This class detrends all spectra. This is done by fitting a polynomial to the spectrum (x value is simply the index of data point), and then
subtracting the fit curve from the spectrum.
The degree of the polynomial can be choosen between 0 (the mean is subtracted), 1 (a fitted straight line is subtracted).
|
| PolynomialDetrendingSerializationSurrogate0 | |
| PolynomialDetrendingBase |
This class detrends all spectra. This is done by fitting a polynomial to the spectrum (x value is simply the index of data point), and then
subtracting the fit curve from the spectrum.
The degree of the polynomial can be choosen between 0 (the mean is subtracted), 1 (a fitted straight line is subtracted).
|
| SNIP_Base | |
| SNIP_Linear |
SNIP algorithm for background estimation on linear (unmodified) data. SNIP = Statistical sensitive Non-Linear Iterative Procedure.
|
| SNIP_LinearSerializationSurrogate0 | |
| SNIP_LogLog |
SNIP algorithm for background estimation (SNIP = Statistical sensitive Non-Linear Iterative Procedure).
Before execution of the algorithm, the data are twice logarithmized, as described in Ref.[1], and backtransformed afterwards.
|
| SNIP_LogLogSerializationSurrogate0 | |
| SSProb |
This class detrends a spectrum. This is done by fitting a smoothing spline through the spectrum.
Then the spectral data that lies significantly above the spline are excluded from the spline.
The process is repeated until the resulting curve changes no more.
|
| SSProbSerializationSurrogate0 |
2024-04-16 V0 initial version.
|
| SSProbBase |
This class detrends a spectrum. This is done by fitting a smoothing spline through the spectrum.
Then the spectral data that lies significantly above the spline are excluded from the spline.
The process is repeated until the resulting curve changes no more.
|
| XToXLine | |
| XToXLineSerializationSurrogate0 | |
| XToXLineBase | |
Interfaces | | Interface | Description |
|---|
 | IBaselineEstimation |
Interface to all baseline estimation algorithms for simple (1D) spectra.
|
Enumerations 
