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/FILTERING/SMOOTHING/GaussFilterAlgorithm.h>

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 an two data arrays containing data. 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 . 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 of the Gaussian-window with length are calculated from the gaussian distribution

where represents the window area and 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 ( OpenMS::Interfaces::SpectrumPtr  spectrum )

inline

Smoothes an Spectrum containing profile data.

filter() [2/3]

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

inline

Smoothes an Chromatogram containing profile data.

filter() [3/3]

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

inline

Smoothes an two data arrays containing data.

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

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 .

spacing_

double spacing_

protected

The spacing of the pre-tabulated kernel coefficients.

use_ppm_tolerance_

bool use_ppm_tolerance_

protected