result.datagenerator¶

DataGenerator.

DataGeneratorFunction ¶

DataGeneratorFunction.

DataGeneratorIndexingFunction(index, dimension='_time')

DataGeneratorIndexingFunction.

Initialize DataGeneratorIndexingFunction.

Parameters:

Name	Type	Description	Default
`index`	`int`	Index to select on the dimension.	required
`dimension`	`str`	Dimension to reduce.	`'_time'`

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.

Initialize DataGenerator.

Parameters:

Name	Type	Description	Default
`f`	`DataGeneratorFunction`	Function applied to the data.	required
`xresults`	`dict[str, XResult]`	Results to process.	required
`dsets`	`dict[str, DataSet] \| None`	Datasets to process.	`None`

process()

Process the data generator.

Returns:

Type	Description
`dict[str, XResult]`	Processed results.