sbmlsim.combine.datagenerator
¶
DataGenerator.
Module Contents¶
Classes¶
DataGeneratorFunction. |
|
DataGeneratorIndexingFunction. |
|
DataGenerator. |
- class sbmlsim.combine.datagenerator.DataGeneratorFunction[source]¶
DataGeneratorFunction.
- abstract __call__(xresults, dsets=None)[source]¶
Call the function.
- Parameters:
xresults (Dict[str, sbmlsim.xresult.XResult]) –
dsets (Dict[str, sbmlsim.data.DataSet]) –
- 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:
f (DataGeneratorFunction) –
xresults (Dict[str, sbmlsim.xresult.XResult]) –
dsets (Dict[str, sbmlsim.data.DataSet]) –