PSLPFormulation#

class pyopenms.PSLPFormulation#

Bases: object

Cython implementation of _PSLPFormulation

Original C++ documentation is available here

– Inherits from [‘DefaultParamHandler’]

__init__()#

Overload:

__init__(self) → None

Overload:

__init__(self, in_0: PSLPFormulation) → None

Methods

__init__

Overload:

createAndSolveILPForInclusionListCreation(...)

Find a set of precursors, so that the protein coverage is maximal and that the number of precursors per bin is not exceeded

createAndSolveILPForKnownLCMSMapFeatureBased(...)

Encode ILP formulation for a given LC-MS map, but unknown protein sample

getDefaults(self)

Returns the default parameters

getLPSolver(self)

getName(self)

Returns the name

getParameters(self)

Returns the parameters

getSubsections(self)

setLPSolver(self, solver)

setName(self, in_0)

Sets the name

setParameters(self, param)

Sets the parameters

solveILP(self, solution_indices)

Solve the ILP

updateRTConstraintsForSequentialILP(self, ...)

updateStepSizeConstraint(self, iteration, ...)

createAndSolveILPForInclusionListCreation(self, preprocessing: PrecursorIonSelectionPreprocessing, ms2_spectra_per_rt_bin: int, max_list_size: int, precursors: FeatureMap, solve_ILP: bool) → None#

Find a set of precursors, so that the protein coverage is maximal and that the number of precursors per bin is not exceeded

createAndSolveILPForKnownLCMSMapFeatureBased(self, features: FeatureMap, experiment: MSExperiment, variable_indices: List[IndexTriple], mass_ranges: List[List[List[int, int]]], charges_set: Set[int], ms2_spectra_per_rt_bin: int, solution_indices: List[int]) → None#

Encode ILP formulation for a given LC-MS map, but unknown protein sample

Parameters:

  • features – FeatureMap with all possible precursors

  • experiment – Input raw data

  • variable_indices – Assignment of feature indices and ILP variables

  • mass_ranges – Feature borders as indices in the raw data

  • charges_set – Allowed charge states

  • ms2_spectra_per_rt_bin – Allowed number of precursors per rt bin

  • solution_indices – Indices of ILP variables that are in the optimal solution

getDefaults(self) → Param#

Returns the default parameters

getLPSolver(self) → int#
getName(self) → bytes | str | String#

Returns the name

getParameters(self) → Param#

Returns the parameters

getSubsections(self) → List[bytes]#
setLPSolver(self, solver: int) → None#
setName(self, in_0: bytes | str | String) → None#

Sets the name

setParameters(self, param: Param) → None#

Sets the parameters

solveILP(self, solution_indices: List[int]) → None#

Solve the ILP

updateRTConstraintsForSequentialILP(self, rt_index: int, ms2_spectra_per_rt_bin: int, max_rt_index: int) → None#
updateStepSizeConstraint(self, iteration: int, step_size: int) → None#