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 pyopenms._pyopenms_5.__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) → Union[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