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

__init__

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