IsotopeModel

class pyopenms.IsotopeModel

Bases: object

Cython implementation of _IsotopeModel

Original C++ documentation is available here

Isotope distribution approximated using linear interpolation

This models a smoothed (widened) distribution, i.e. can be used to sample actual raw peaks (depending on the points you query) If you only want the distribution (no widening), use either EmpiricalFormula::getIsotopeDistribution() // for a certain sum formula or IsotopeDistribution::estimateFromPeptideWeight (double average_weight) // for averagine

Peak widening is achieved by either a Gaussian or Lorentzian shape

__init__()

Overload:

__init__(self) → None

Overload:

__init__(self, in_0: IsotopeModel) → None

Methods

__init__	Overload:
getCenter(self)	Get the center of the Isotope model
getCharge(self)
getFormula(self)	Return the Averagine peptide formula (mass calculated from mean mass and charge -- use .setParameters() to set them)
getIsotopeDistribution(self)	Get the Isotope distribution (without widening) from the last setSamples() call
getOffset(self)	Get the offset of the model
getProductName(self)	Name of the model (needed by Factory)
setOffset(self, offset)	Set the offset of the model
setSamples(self, formula)	Set sample/supporting points of interpolation

Averagines

alias of __Averagines

getCenter(self) → float

Get the center of the Isotope model

This is a m/z-value not necessarily the monoisotopic mass

getCharge(self) → int

getFormula(self) → EmpiricalFormula

Return the Averagine peptide formula (mass calculated from mean mass and charge – use .setParameters() to set them)

getIsotopeDistribution(self) → IsotopeDistribution

Get the Isotope distribution (without widening) from the last setSamples() call

Useful to determine the number of isotopes that the model contains and their position

getOffset(self) → float

Get the offset of the model

getProductName(self) → bytes | str | String

Name of the model (needed by Factory)

setOffset(self, offset: float) → None

Set the offset of the model

The whole model will be shifted to the new offset without being computing all over This leaves a discrepancy which is minor in small shifts (i.e. shifting by one or two standard deviations) but can get significant otherwise. In that case use setParameters() which enforces a recomputation of the model

setSamples(self, formula: EmpiricalFormula) → None

Set sample/supporting points of interpolation