GaussFilterAlgorithm Class Reference

This class represents a Gaussian lowpass-filter which works on uniform as well as on non-uniform profile data. More...

#include <OpenMS/PROCESSING/SMOOTHING/GaussFilterAlgorithm.h>

Collaboration diagram for GaussFilterAlgorithm:

Public Member Functions
	GaussFilterAlgorithm ()
	Constructor. More...
virtual	~GaussFilterAlgorithm ()
	Destructor. More...
bool	filter (OpenMS::Interfaces::SpectrumPtr spectrum)
	Smoothes an Spectrum containing profile data. More...
bool	filter (OpenMS::Interfaces::ChromatogramPtr chromatogram)
	Smoothes an Chromatogram containing profile data. More...
template<typename ConstIterT , typename IterT >
bool	filter (ConstIterT mz_in_start, ConstIterT mz_in_end, ConstIterT int_in_start, IterT mz_out, IterT int_out)
	Smoothes two data arrays. More...
void	initialize (double gaussian_width, double spacing, double ppm_tolerance, bool use_ppm_tolerance)

Protected Member Functions
template<typename InputPeakIterator >
double	integrate_ (InputPeakIterator x, InputPeakIterator y, InputPeakIterator first, InputPeakIterator last)
	Computes the convolution of the raw data at position x and the gaussian kernel. More...

Protected Attributes
std::vector< double >	coeffs_
	Coefficients. More...
double	sigma_
	The standard derivation \( \sigma \). More...
double	spacing_
	The spacing of the pre-tabulated kernel coefficients. More...
bool	use_ppm_tolerance_
double	ppm_tolerance_

Detailed Description

This class represents a Gaussian lowpass-filter which works on uniform as well as on non-uniform profile data.

Gaussian filters are important in many signal processing, image processing, and communication applications. These filters are characterized by narrow bandwidths, sharp cutoffs, and low passband ripple. A key feature of Gaussian filters is that the Fourier transform of a Gaussian is also a Gaussian, so the filter has the same response shape in both the time and frequency domains. The coefficients \( \emph{coeffs} \) of the Gaussian-window with length \( \emph{frameSize} \) are calculated from the gaussian distribution

\[ \emph{coeff}(x) = \frac{1}{\sigma \sqrt{2\pi}} e^{\frac{-x^2}{2\sigma^2}} \]

where \( x=[-\frac{frameSize}{2},...,\frac{frameSize}{2}] \) represents the window area and \( \sigma \) is the standard derivation.

Note

The wider the kernel width the smoother the signal (the more detail information get lost!). Use a gaussian filter kernel which has approximately the same width as your mass peaks, whereas the gaussian peak width corresponds approximately to 8*sigma.

The data must be sorted according to ascending m/z!

Constructor & Destructor Documentation

GaussFilterAlgorithm()

GaussFilterAlgorithm

(

)

Constructor.

~GaussFilterAlgorithm()

virtual ~GaussFilterAlgorithm ( )

virtual

Destructor.

Member Function Documentation

filter() [1/3]

bool filter ( ConstIterT  mz_in_start, ConstIterT  mz_in_end, ConstIterT  int_in_start, IterT  mz_out, IterT  int_out  )

inline

Smoothes two data arrays.

Convolutes the filter and the profile data and writes the results into the output iterators mz_out and int_out.

References OpenMS::Math::ppmToMass().

filter() [2/3]

bool filter ( OpenMS::Interfaces::ChromatogramPtr  chromatogram )

inline

Smoothes an Chromatogram containing profile data.

filter() [3/3]

bool filter ( OpenMS::Interfaces::SpectrumPtr  spectrum )

inline

Smoothes an Spectrum containing profile data.

initialize()

void initialize	(	double	gaussian_width,
		double	spacing,
		double	ppm_tolerance,
		bool	use_ppm_tolerance
	)

integrate_()

double integrate_ ( InputPeakIterator  x, InputPeakIterator  y, InputPeakIterator  first, InputPeakIterator  last  )

inlineprotected

Computes the convolution of the raw data at position x and the gaussian kernel.

References OpenSwath::norm().

Member Data Documentation

coeffs_

std::vector<double> coeffs_

protected

Coefficients.

ppm_tolerance_

double ppm_tolerance_

protected

sigma_

double sigma_

protected

The standard derivation \( \sigma \).

spacing_

double spacing_

protected

The spacing of the pre-tabulated kernel coefficients.

use_ppm_tolerance_

bool use_ppm_tolerance_

protected