sbmlsim.result.datagenerator

DataGenerator.

Module Contents

Classes

DataGeneratorFunction	DataGeneratorFunction.
DataGeneratorIndexingFunction	DataGeneratorIndexingFunction.
DataGenerator	DataGenerator.

class sbmlsim.result.datagenerator.DataGeneratorFunction

DataGeneratorFunction.

abstract __call__(self, xresults, dsets=None)

Call the function.

Parameters

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

• dsets (Dict[str, sbmlsim.data.DataSet]) –

Return type

Dict[str, sbmlsim.result.XResult]

class sbmlsim.result.datagenerator.DataGeneratorIndexingFunction(index, dimension='_time')

Bases: DataGeneratorFunction

DataGeneratorIndexingFunction.

Parameters

• index (int) –

• dimension (str) –

__call__(self, xresults, dsets=None)

Reduce based on ‘_time’ dimension with given index.

Parameters

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

Return type

Dict[str, sbmlsim.result.XResult]

class sbmlsim.result.datagenerator.DataGenerator(f, xresults, dsets=None)

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

• f (DataGeneratorFunction) –

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

• dsets (Dict[str, sbmlsim.data.DataSet]) –

process(self)

Process the data generator.

Return type

sbmlsim.result.XResult