MZTrafoModel#

class pyopenms.MZTrafoModel#

Bases: object

Create and apply models of a mass recalibration function

The input is a list of calibration points (ideally spanning a wide m/z range to prevent extrapolation when applying to model)

Models (LINEAR, LINEAR_WEIGHTED, QUADRATIC, QUADRATIC_WEIGHTED) can be trained using CalData points (or a subset of them) Calibration points can have different retention time points, and a model should be build such that it captures the local (in time) decalibration of the instrument, i.e. choose appropriate time windows along RT to calibrate the spectra in this RT region From the available calibrant data, a model is build. Later, any uncalibrated m/z value can be fed to the model, to obtain a calibrated m/z

The input domain can either be absolute mass differences in [Th], or relative differences in [ppm] The models are build based on this input

Outlier detection before model building via the RANSAC algorithm is supported for LINEAR and QUADRATIC models

__init__() → None#
__init__(arg: bool, /) → None
__init__(arg: pyopenms.MZTrafoModel, /) → None

Methods

__init__()

enumToName(mt)

findNearest(tms, rt)

Find the model nearest to the given RT

getCoefficients()

Get model coefficients

getRT()

Get RT associated with the model (training region)

isTrained()

Returns true if the model have coefficients (i.e. was trained successfully).

isValidModel(trafo)

nameToEnum(name)

predict(mz)

Apply the model to an uncalibrated m/z value

setCoefficientLimits(offset, scale, power)

setCoefficients(rhs)

Overloaded function.

setRANSACParams(p)

setRANSACSeed(seed)

toString()

train(cd, md, use_RANSAC[, rt_left, rt_right])

Overloaded function.
class MODELTYPE(*values)#

Bases: Enum

Model type for m/z calibration transformation

LINEAR = 0#
LINEAR_WEIGHTED = 1#
QUADRATIC = 2#
QUADRATIC_WEIGHTED = 3#
SIZE_OF_MODELTYPE = 4#
getMapping()#

Returns a dict mapping int values to enum member name strings.

static enumToName(mt: pyopenms.MZTrafoModel.MODELTYPE) → str#
static findNearest(tms: collections.abc.Sequence[pyopenms.MZTrafoModel], rt: float) → int#

Find the model nearest to the given RT

getCoefficients() → tuple#

Get model coefficients

The model must be trained before; Exception is thrown otherwise!

Returns:

Tuple of (intercept, slope, power); power is the coefficient for x*x (0 for linear models)

getRT() → float#

Get RT associated with the model (training region)

isTrained() → bool#

Returns true if the model have coefficients (i.e. was trained successfully)

static isValidModel(trafo: pyopenms.MZTrafoModel) → bool#
static nameToEnum(name: str) → pyopenms.MZTrafoModel.MODELTYPE#
predict(mz: float) → float#

Apply the model to an uncalibrated m/z value

Make sure the model was trained (train()) and is valid (isValidModel()) before calling this function!

Applies the function y = intercept + slope*mz + power*mz^2 and returns y

Parameters:

mz – The uncalibrated m/z value

Returns:

The calibrated m/z value

static setCoefficientLimits(offset: float, scale: float, power: float) → None#
setCoefficients(rhs: pyopenms.MZTrafoModel) → None#
setCoefficients(intercept: float, slope: float, power: float) → None

Overloaded function.

  1. setCoefficients(rhs: pyopenms.MZTrafoModel) -> None

Copy model coefficients from another model

  1. setCoefficients(intercept: float, slope: float, power: float) -> None

Manually set model coefficients

Can be used instead of train(), so manually set coefficients It must be exactly three values. If you want a linear model, set ‘power’ to zero If you want a constant model, set slope to zero in addition

Parameters:

  • intercept – The offset

  • slope – The slope

  • power – The x*x coefficient (for quadratic models)

static setRANSACParams(p: pyopenms.RANSACParam) → None#
static setRANSACSeed(seed: int) → None#
toString() → str#
train(cd: pyopenms.CalibrationData, md: pyopenms.MZTrafoModel.MODELTYPE, use_RANSAC: bool, rt_left: float = -1.7976931348623157e+308, rt_right: float = 1.7976931348623157e+308) → bool#
train(error_mz: collections.abc.Sequence[float], theo_mz: collections.abc.Sequence[float], weights: collections.abc.Sequence[float], md: pyopenms.MZTrafoModel.MODELTYPE, use_RANSAC: bool) → bool

Overloaded function.

  1. train(cd: pyopenms.CalibrationData, md: pyopenms.MZTrafoModel.MODELTYPE, use_RANSAC: bool, rt_left: float = -1.7976931348623157e+308, rt_right: float = 1.7976931348623157e+308) -> bool

Train a model using calibrant data

If the CalibrationData was created using peak groups (usually corresponding to mass traces), the median for each group is used as a group representative. This is more robust, and reduces the number of data points drastically, i.e. one value per group

Internally, these steps take place:

  • apply RT filter

  • [compute median per group] (only if groups were given in ‘cd’)

  • set Model’s rt position

  • call train() (see overloaded method)

Parameters:

  • cd – List of calibrants

  • md – Type of model (linear, quadratic, …)

  • use_RANSAC – Remove outliers before computing the model?

  • rt_left – Filter ‘cd’ by RT; all calibrants with RT < ‘rt_left’ are removed

  • rt_right – Filter ‘cd’ by RT; all calibrants with RT > ‘rt_right’ are removed

Returns:

True if model was build, false otherwise

  1. train(error_mz: collections.abc.Sequence[float], theo_mz: collections.abc.Sequence[float], weights: collections.abc.Sequence[float], md: pyopenms.MZTrafoModel.MODELTYPE, use_RANSAC: bool) -> bool

Train a model using calibrant data

Given theoretical and observed mass values (and corresponding weights), a model (linear, quadratic, …) is build Outlier removal is applied before The ‘obs_mz’ can be either given as absolute masses in [Th] or relative deviations in [ppm] The MZTrafoModel must be constructed accordingly (see constructor). This has no influence on the model building itself, but rather on how ‘predict()’ works internally

Outlier detection before model building via the RANSAC algorithm is supported for LINEAR and QUADRATIC models

Internally, these steps take place:

  • [apply RANSAC] (depending on ‘use_RANSAC’)

  • build model and store its parameters internally

Parameters:

  • error_mz – Observed Mass error (in ppm or Th)

  • theo_mz – Theoretical m/z values, corresponding to ‘error_mz’

  • weights – For weighted models only: weight of calibrants; ignored otherwise

  • md – Type of model (linear, quadratic, …)

  • use_RANSAC – Remove outliers before computing the model?

Returns:

True if model was build, false otherwise