Blop
++ a BeamLine Optimization Package +
+
+
+
+ What is Blop?
++ Blop is a Python library for performing optimization for beamline + experiments. It is designed to integrate nicely with the Bluesky + ecosystem and primarily targets rapid beamline data acquisition + and control. +
++ Our goal is to provide a simple and practical data-driven + optimization interface for beamline experiments. +
+
+
+
+ [Visualization Placeholder]
+
+ + +
Learn More!
+
+
+ {# We define the list so additional items can be easily added #}
+ {% set nav_items = [
+ ("Tutorials", "tutorials.html", "Step-by-step guides to get started with Blop fundamentals and basic workflows"),
+ ("How-to", "howto.html", "Practical recipes and solutions for specific beamline optimization tasks"),
+ ("References", "references.html", "Complete API documentation, class references, and technical specifications"),
+ ("Release History", "release-history.html", "Version updates, new features, bug fixes, and changelog for v" + release)
+ ] %}
+
+ {% for title, link, description in nav_items %}
+
+
+
+ {{ title }}
+
+
+ {% endfor %}
+
+ {{ description }}
+
+ + +
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},
+ }
+
+