Beam Quality Detector

For Automatic Detection of Laser Beam Quality

Beam Quality Detector is a Python API that make it easy to automate laser beam image aberration detection for all cameras that captures laser beam images.

Two main parts of design: threshold reference and CNN model. Threshold Reference is used to identify the aberration laser images, and if the image is identified as abnormal, the CNN model will output which category it is (Hot Spot, Clipped Edge, or Airy Ring)

Features

Detect Laser Beam Image Aberration
Classify Aberration Category
Compatible to any image size/shape
Data Processing from xtc format file
Image Transformation

Tech & Dependency

Beam Quality Detector requires following packages & dependencies to run properly:

psana - used for LCLS internal data analysis/processing tool
h5py - a container for datasets and groups.
torch - for CNN model building and running to classify image

Usage

The running environment uses conda. Install the dependencies and devDependencies and start jupyter-notebook, more usage examples can be viewed in use_case_example.ipynb.

conda env create -f environment.yml
conda activate laser_beam

License