Holography and Light Scattering in Python¶
HoloPy is a python based tool for working with digital
holograms and light scattering. HoloPy can be used to analyze holograms in two complementary ways:
- Backward propagation of light from a digital hologram to reconstruct 3D volumes.
- This approach requires no prior knowledge about the scatterer
- Forward propagation of light from a :ref: scattering calculation <calc_tutorial> of a predetermined scatterer.
- Comparison to a measured hologram with :ref: Bayesian inference <infer_tutorial> allows precise measurement of scatterer properties and position.
HoloPy provides a powerful and user-friendly python interface to fast scattering and optical propagation theories implemented in Fortran and C code. It also provides a set of flexible objects that make it easy to describe and analyze data from complex experiments or simulations.
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
- Getting Started
- Loading Data
- Reconstructing Data (Numerical Propagation)
- Reconstructing Point Source Holograms (Numerical Propagation)
- Scattering Calculations
- Scattering from Arbitrary Structures with DDA
- Bayesian inference of Parameter Values
- Fitting Models to Data
- Developer’s Guide
- HoloPy Tools
- 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