Welcome to PyOptEx documentation!
Date: May 14, 2026
Version: 1.2.1
PyOptEx (or Python Optimal Experiments) is a package designed to create optimal design of experiments with Python. It is fully open source and can be used for any purpose.
The package is designed for both engineers, and design of experiment researchers. Engineers can use the precreated functions to generate designs for their problems. Researchers can easily develop new metrics (criteria) and test them. If you would like a refresher on the concept of optimal design of experiments, see Optimal design of experiments (DoE).
To generate experimental designs, there are two main options:
Fixed structure: These designs have a fixed number of runs and fixed randomization structure, known upfront. Well-known designs include split-plot, strip-plot, and regular staggered-level designs. A specialization is also included for splitk-plot designs using the update formulas as described in Born and Goos (2025). Go to Create your first design and Creating a splitk-plot design for an example respectively.
Cost-optimal designs: These design generation algorithms follow a new DoE philosophy. Instead of fixing the number of runs and randomization structure, the algorithm optimizes directly based on the underlying resource constraints. The user must only specify a budget and a function which computes the resource consumption of a design. Go to Creating a cost-optimal design for an example. The currently implemented algorithm is CODEX.
The overview of the PyOptEx package.
See the design of experiments Quickstart for more information on how to generate different kinds of designs. See Customization for a more detailed explanation on how to tune and customize each algorithm. Reseachers can find more information here on how to design custom criteria. The example scenarios are noted in Example scenarios. Finally, see Performance for some tips on how to make the algorithm run faster.
To analyze the data after the experiment, have a look at the analysis Quickstart.
Main features
The first complete Python package for optimal design of experiments. Model everything including continuous factors, categorical factors, mixtures, blocked experiments, split-plot experiments, staggered-level experiments.
Intuitive design of experiments with cost-optimal designs for everyone. No longer requires expert statistical knowledge before creating experiments.
Accounts for any constraint you require. Not only can you choose the randomization structure manually, or let the cost-optimal design algorithms figure it out automatically, you can also specify the physically possible factor combinations for a run.
Augmenting designs was never easier. Simply read your initial design to a pandas dataframe and augment it by passing it as a prior.
Customize any part of the algorithm, including the optimization criteria (metrics), linear model, encoding of the categorical factors, and much more.
Directly optimize for Bayesian a-priori variance ratios in designs with hard-to-change factors.
High-performance model selection using SAMS (simulated annealing model selection) (Wolters and Bingham, 2012).