Blop#

a BLuesky Optimization Package

What is Blop?#

Blop is a Python library for performing optimization for Bluesky experiments. It is designed to integrate nicely with the Bluesky ecosystem and primarily targets rapid data acquisition and control.

Our goal is to provide a simple and practical data-driven optimization interface for Bluesky-driven experimentation.

Image of beamline optimization done through blop

Autonomous alignment visualization using Bayesian optimization.

Installation#

Via PyPI

Standard (GPU support):

$ pip install blop

CPU-only (containers, CI/CD, laptops):

$ pip install uv
$ uv pip install blop[cpu]

Via Conda-forge

Standard:

$ conda install -c conda-forge blop

CPU-only:

$ conda install -c conda-forge blop pytorch cpuonly -c pytorch

For additional installation instructions, refer to the Installation guide.

Learn More!#

Tutorials

Step-by-step guides to get started with Blop fundamentals and basic workflows.

How-To Guides

Practical recipes and solutions for specific beamline optimization tasks.

References

Complete API documentation, class references, and technical specifications.

Release History

Version updates, new features, bug fixes, and changelog for the current release.

References#

If you use this package in your work, please cite the following paper:

Morris, T. W., Rakitin, M., Du, Y., Fedurin, M., Giles, A. C., Leshchev, D., Li, W. H., Romasky, B., Stavitski, E., Walter, A. L., Moeller, P., Nash, B., & Islegen-Wojdyla, A. (2024). A general Bayesian algorithm for the autonomous alignment of beamlines. Journal of Synchrotron Radiation, 31(6), 1446–1456. https://doi.org/10.1107/S1600577524008993

BibTeX:

@Article{Morris2024,
     author   = {Morris, Thomas W. and Rakitin, Max and Du, Yonghua and Fedurin, Mikhail and Giles, Abigail C. and Leshchev, Denis and Li, William H. and Romasky, Brianna and Stavitski, Eli and Walter, Andrew L. and Moeller, Paul and Nash, Boaz and Islegen-Wojdyla, Antoine},
     journal  = {Journal of Synchrotron Radiation},
     title    = {A general Bayesian algorithm for the autonomous alignment of beamlines},
     year     = {2024},
     month    = {Nov},
     number   = {6},
     pages    = {1446--1456},
     volume   = {31},
     doi      = {10.1107/S1600577524008993},
     keywords = {Bayesian optimization, automated alignment, synchrotron radiation, digital twins, machine learning},
     url      = {https://doi.org/10.1107/S1600577524008993},
}