Source code for sbmlsim.combine.datagenerator

from typing import Dict

from import DataSet
from sbmlsim.xresult import XResult

[docs]class DataGeneratorFunction: """DataGeneratorFunction."""
[docs] def __call__( self, xresults: Dict[str, XResult], dsets: Dict[str, DataSet] = None ) -> Dict[str, XResult]: """Call the function.""" raise NotImplementedError
[docs]class DataGeneratorIndexingFunction(DataGeneratorFunction): """DataGeneratorIndexingFunction.""" def __init__(self, index: int, dimension: str = "_time"): """Initialize DataGeneratorIndexingFunction.""" self.index = index self.dimension = dimension
[docs] def __call__(self, xresults: Dict[str, XResult], dsets=None) -> Dict[str, XResult]: """Reduce based on '_time' dimension with given index.""" results = {} for key, xres in xresults.items(): xds_new = xres.xds.isel({self.dimension: self.index}) xres_new = XResult(xdataset=xds_new, uinfo=xres.uinfo) results[key] = xres_new return results
[docs]class DataGenerator: """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. """ def __init__( self, f: DataGeneratorFunction, xresults: Dict[str, XResult], dsets: Dict[str, DataSet] = None, ): """Initialize DataGenerator.""" self.xresults = xresults self.dsets = dsets self.f = f
[docs] def process(self) -> XResult: """Process the data generator.""" return self.f(xresults=self.xresults, dsets=self.dsets)