Holography and Light Scattering in Python¶
|Date:||December 12, 2016|
HoloPy is a python based tool for working with digital
holograms and light scattering. HoloPy can:
- Load images, associate them with experimental metadata, and visualize loaded or calculated images.
- Reconstruct 3D volumes from digital holograms
- Perform Scattering Calculations with fast Fortran/C code
- Compute Holograms, electric fields, scattered intensity, cross sections, ...
- From spheres, clusters of spheres, and arbitrary structures (using DDA)
- Use Bayesian analysis methods to infer a scattering model that is most consistent with experimental data.
- Make fast, precise measurements to fit scattering models (based on the above structures) to experimental data, based on an inital guess.
HoloPy provides a powerful and user-friendly interface to scattering and optical propagation theories. It also provides a set of flexible objects that make it easy to describe and analyze data from complex experiments or simulations.
The easiest way to see what HoloPy is all about is to jump to the examples in our User Guide.
HoloPy started as a project in the Manoharan Lab at Harvard University. If you use HoloPy, you may wish to cite one or more of the sources listed in References and credits. We also encourage you to sign up for our User Mailing List to keep up to date on releases, answer questions, and benefit from other users’ questions.
- User Guide
- holopy package
- References and credits
HoloPy is based upon work supported by the National Science Foundation under Grant No. CBET-0747625 and performed in the Manoharan Lab at Harvard University