PrecursorIonSelection

class pyopenms.PrecursorIonSelection

Bases: object

Cython implementation of _PrecursorIonSelection

Original C++ documentation is available here

– Inherits from [‘DefaultParamHandler’]

__init__()

Overload:

__init__(self) → None

Overload:

__init__(self, in_0: PrecursorIonSelection) → None

Methods

__init__

Overload:

getDefaults(self)

Returns the default parameters

getLPSolver(self)

getMaxScore(self)

getName(self)

Returns the name

getNextPrecursors

Overload:

getParameters(self)

Returns the parameters

getSubsections(self)

rescore(self, features, new_pep_ids, ...)

Change scoring of features using peptide identifications from all spectra

reset(self)

setLPSolver(self, solver)

setMaxScore(self, max_score)

setName(self, in_0)

Sets the name

setParameters(self, param)

Sets the parameters

simulateRun(self, features, pep_ids, ...)

Simulate the iterative precursor ion selection

sortByTotalScore(self, features)

Sort features by total score
PrecursorIonSelection_Type

alias of pyopenms._pyopenms_4.__PrecursorIonSelection_Type

getDefaults(self) → Param

Returns the default parameters

getLPSolver(self) → int
getMaxScore(self) → float
getName(self) → Union[bytes, str, String]

Returns the name

getNextPrecursors()

Overload:

getNextPrecursors(self, features: FeatureMap, next_features: FeatureMap, number: int) → None

Returns features with highest score for MS/MS

Parameters

  • features – FeatureMap with all possible precursors

  • next_features – FeatureMap with next precursors

  • number – Number of features to be reported

Overload:

getNextPrecursors(self, solution_indices: List[int], variable_indices: List[IndexTriple], measured_variables: Set[int], features: FeatureMap, new_features: FeatureMap, step_size: int, ilp: PSLPFormulation) → None
getParameters(self) → Param

Returns the parameters

getSubsections(self) → List[bytes]
rescore(self, features: FeatureMap, new_pep_ids: List[PeptideIdentification], prot_ids: List[ProteinIdentification], preprocessed_db: PrecursorIonSelectionPreprocessing, check_meta_values: bool) → None

Change scoring of features using peptide identifications from all spectra

Parameters

  • features – FeatureMap with all possible precursors

  • new_pep_ids – Peptide identifications

  • prot_ids – Protein identifications

  • preprocessed_db – Information from preprocessed database

  • check_meta_values – True if the FeatureMap should be checked for the presence of required meta values

reset(self) → None
setLPSolver(self, solver: int) → None
setMaxScore(self, max_score: float) → None
setName(self, in_0: Union[bytes, str, String]) → None

Sets the name

setParameters(self, param: Param) → None

Sets the parameters

simulateRun(self, features: FeatureMap, pep_ids: List[PeptideIdentification], prot_ids: List[ProteinIdentification], preprocessed_db: PrecursorIonSelectionPreprocessing, path: Union[bytes, str, String], experiment: MSExperiment, precursor_path: Union[bytes, str, String]) → None

Simulate the iterative precursor ion selection

Parameters

  • features – FeatureMap with all possible precursors

  • new_pep_ids – Peptide identifications

  • prot_ids – Protein identifications

  • preprocessed_db – Information from preprocessed database

  • step_size – Number of MS/MS spectra considered per iteration

  • path – Path to output file

sortByTotalScore(self, features: FeatureMap) → None

Sort features by total score