sbmlsim.combine.datagenerator

DataGenerator.

Module Contents

Classes

DataGeneratorFunction

DataGeneratorFunction.

DataGeneratorIndexingFunction

DataGeneratorIndexingFunction.

DataGenerator

DataGenerator.

class sbmlsim.combine.datagenerator.DataGeneratorFunction[source]

DataGeneratorFunction.

abstract __call__(xresults, dsets=None)[source]

Call the function.

Parameters
Return type

Dict[str, sbmlsim.xresult.XResult]

class sbmlsim.combine.datagenerator.DataGeneratorIndexingFunction(index, dimension='_time')[source]

Bases: DataGeneratorFunction

DataGeneratorIndexingFunction.

Parameters
  • index (int) –

  • dimension (str) –

__call__(xresults, dsets=None)[source]

Reduce based on ‘_time’ dimension with given index.

Parameters

xresults (Dict[str, sbmlsim.xresult.XResult]) –

Return type

Dict[str, sbmlsim.xresult.XResult]

class sbmlsim.combine.datagenerator.DataGenerator(f, xresults, dsets=None)[source]

DataGenerator.

DataGenerators allow to postprocess existing data. This can be a variety of operations.

  • Slicing: reduce the dimension of a given XResult, by slicing a subset on a given dimension

  • Cumulative processing: mean, sd, …

  • Complex processing, such as pharmacokinetics calculation.

Parameters
process()[source]

Process the data generator.

Return type

sbmlsim.xresult.XResult