A computational experiment, inheriting from Experiment. Experiments are run at a point in a multi-dimensional parameter space, and should be designed to be repeatable.
experiment combinators
Experiments that wrap-up other, underlying experiments and perform them in some way, perhaps repeating them or summarising or re-writing their results. They allow common experimental patterns to be coded.
experimental configuration
A list of pairs of an experiment and the parameters at which it will be run, created according to an experimental design.
experimental design
The way in which a set of parameters is converted into points at which experiments are run.
experimental parameters
The values used to position an individual experimental run in the “space” of all experiments. Each experiment has its own parameters, which it can use to configure itself and perform set-up (see The lifecycle of an experiment).
experimental results
The collection of values returned by an experimental run.
A computational laboratory co-ordinating the execution of multiple experiments, inheriting from Lab.
Additional information about an experiment, returned as part of a results dict.
An immutable and often persistent store experimental results and metadata, inheriting from LabNotebook.
parameter space
The set of experimental parameters at which experiments will be run. The parameter space is defined by a Design,
results dict
A dict structured according to a particular convention. The dict uses three top-level keys, defined by the Experiment class, for the parameter values of the experiment, the experimental results, and some metadata values. Each of these top-level keys themselves map to a hash of further values: for some experiments, the experimental results key may refer to a list of hashes.