Manual

All public medeq subroutines are fully documented here, along with copy-pastable examples. The base functionality is summarised below. You can also use the Search bar in the top left to go directly to what you need.

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MED

These are the main objects you’ll work with:

medeq.create_parameters([variables, ...])	Create a pandas.DataFrame storing MED free parameters' names, bounds.
medeq.MED(parameters[, response_names, ...])	Autonomously explore system responses and discover underlying physical laws or correlations.

Samplers

For experimental planning, this library offers a few different parameter sampling strategies:

medeq.LatticeSampler(d[, seed])	Parameter sampler following a regular lattice - this also corresponds to a full factorial design of experiments.
medeq.RandomSampler(d[, seed])	Parameter sampler using uniform random distributions.
medeq.DVASampler(d[, seed])	Parameter sampler that targets the most uncertain regions while maximising the area covered.

Auxiliary

medeq.sampler(f)	Decorator making a user-defined function a MED sampler.
medeq.med.MEDPaths(directory)	Structure handling IO and storing all paths relevant for MED.