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
![\[ \emph{coeff}(x) = \frac{1}{\sigma \sqrt{2\pi}} e^{\frac{-x^2}{2\sigma^2}} \]](form_67.png)
where ![$ x=[-\frac{frameSize}{2},...,\frac{frameSize}{2}] $](form_68.png)
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()
Constructor.
◆ ~GaussFilterAlgorithm()
|
virtual |
Destructor.
Member Function Documentation
◆ filter() [1/3]
|
inline |
Smoothes an Spectrum containing profile data.
◆ filter() [2/3]
|
inline |
Smoothes an Chromatogram containing profile data.
◆ filter() [3/3]
|
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_()
|
inlineprotected |
Computes the convolution of the raw data at position x and the gaussian kernel.
References OpenSwath::norm().
Member Data Documentation
◆ coeffs_
|
protected |
Coefficients.
◆ ppm_tolerance_
|
protected |
◆ sigma_
|
protected |
The standard derivation .
◆ spacing_
|
protected |
The spacing of the pre-tabulated kernel coefficients.
◆ use_ppm_tolerance_
|
protected |