IsotopeWaveletTransform#
- class pyopenms.IsotopeWaveletTransform#
Bases:
object
Cython implementation of _IsotopeWaveletTransform[_Peak1D]
Original C++ documentation is available here
- __init__()#
Overload:
- __init__(self, min_mz: float, max_mz: float, max_charge: int, max_scan_size: int, hr_data: bool, intenstype: bytes | str | String) None
Overload:
- __init__(self, in_0: IsotopeWaveletTransform) None
Methods
Overload:
computeMinSpacing
(self, c_ref)getLinearInterpolation
(self, mz_a, intens_a, ...)Computes a linear (intensity) interpolation
getMaxScanSize
(self)getMinSpacing
(self)getSigma
(self)getTransform
(self, c_trans, c_ref, c)Computes the isotope wavelet transform of charge state c
getTransformHighRes
(self, c_trans, c_ref, c)Computes the isotope wavelet transform of charge state c
identifyCharge
(self, candidates, ref, ...)Given an isotope wavelet transformed spectrum 'candidates', this function assigns to every significant pattern its corresponding charge state and a score indicating the reliability of the prediction.
initializeScan
(self, c_ref, c)mapSeeds2Features
(self, map_, RT_votes_cutoff)Filters the candidates further more and maps the internally used data structures to the OpenMS framework
setSigma
(self, sigma)updateBoxStates
(self, map_, scan_index, ...)A function keeping track of currently open and closed sweep line boxes This function is used by the isotope wavelet feature finder and must be called for each processed scan
- computeMinSpacing(self, c_ref: MSSpectrum) None #
- getLinearInterpolation(self, mz_a: float, intens_a: float, mz_pos: float, mz_b: float, intens_b: float) float #
Computes a linear (intensity) interpolation
- Parameters:
mz_a – The m/z value of the point left to the query
intens_a – The intensity value of the point left to the query
mz_pos – The query point
mz_b – The m/z value of the point right to the query
intens_b – The intensity value of the point left to the query
- getMaxScanSize(self) int #
- getMinSpacing(self) float #
- getSigma(self) float #
- getTransform(self, c_trans: MSSpectrum, c_ref: MSSpectrum, c: int) None #
Computes the isotope wavelet transform of charge state c
- Parameters:
c_trans – The transform
c_ref – The reference spectrum
c – The charge state minus 1 (e.g. c=2 means charge state 3) at which you want to compute the transform
- getTransformHighRes(self, c_trans: MSSpectrum, c_ref: MSSpectrum, c: int) None #
Computes the isotope wavelet transform of charge state c
- Parameters:
c_trans – The transform
c_ref – The reference spectrum
c – The charge state minus 1 (e.g. c=2 means charge state 3) at which you want to compute the transform
- identifyCharge(self, candidates: MSSpectrum, ref: MSSpectrum, scan_index: int, c: int, ampl_cutoff: float, check_PPMs: bool) None #
Given an isotope wavelet transformed spectrum ‘candidates’, this function assigns to every significant pattern its corresponding charge state and a score indicating the reliability of the prediction. The result of this process is stored internally. Important: Before calling this function, apply updateRanges() to the original map
- Parameters:
candidates – A isotope wavelet transformed spectrum. Entry “number i” in this vector must correspond to the charge-“(i-1)”-transform of its mass signal. (This is exactly the output of the function getTransforms.)
ref – The reference scan (the untransformed raw data) corresponding to candidates
c – The corresponding charge state minus 1 (e.g. c=2 means charge state 3)
scan_index – The index of the scan (w.r.t. to some map) currently under consideration
ampl_cutoff – The thresholding parameter. This parameter is the only (and hence a really important) parameter of the isotope wavelet transform. On the basis of ampl_cutoff the program tries to distinguish between noise and signal. Please note that it is not a “simple” hard thresholding parameter in the sense of drawing a virtual line in the spectrum, which is then used as a guillotine cut. Maybe you should play around a bit with this parameter to get a feeling about its range. For peptide mass fingerprints on small data sets (like single MALDI-scans e.g.), it makes sense to start ampl_cutoff=0 or even ampl_cutoff=-1, indicating no thresholding at all. Note that also ampl_cutoff=0 triggers (a moderate) thresholding based on the average intensity in the wavelet transform
check_PPMs – If enabled, the algorithm will check each monoisotopic mass candidate for its plausibility by computing the ppm difference between this mass and the averagine model
- initializeScan(self, c_ref: MSSpectrum, c: int) None #
- mapSeeds2Features(self, map_: MSExperiment, RT_votes_cutoff: int) FeatureMap #
Filters the candidates further more and maps the internally used data structures to the OpenMS framework
- Parameters:
map – The original map containing the data set to be analyzed
max_charge – The maximal charge state under consideration
RT_votes_cutoff – See the IsotopeWaveletFF class
- setSigma(self, sigma: float) None #
- updateBoxStates(self, map_: MSExperiment, scan_index: int, RT_interleave: int, RT_votes_cutoff: int, front_bound: int, end_bound: int) None #
A function keeping track of currently open and closed sweep line boxes This function is used by the isotope wavelet feature finder and must be called for each processed scan
- Parameters:
map – The original map containing the data set to be analyzed
scan_index – The index of the scan currently under consideration w.r.t. its MS map This information is necessary to sweep across the map after each scan has been evaluated
RT_votes_cutoff – See the IsotopeWaveletFF class