For data analysis, we have developed several software packages that we use as our daily driver.


Atomic force microscopy (AFM)

We are working on improving the user experience for analyzing AFM data of cells and tissues. Besides the automation of fitting AFM force-distance data with commonly-used models, we are also developing a machine-learning approach that allows the classification of AFM data according to data quality.

  • PyJibe (docs/sources): Graphical application for analyzing AFM force-distance data (based on nanite).
  • nanite (docs/sources): Python package for loading, fitting, and rating AFM force-distance data.


Brillouin microscopy (BM)

Our acquisition and analysis software are available on GitHub.


Optical diffraction tomography (ODT)

We have introduced the first publicly available library for ODT and are developing additional tools for ODT analysis. For a full list, please visit our dedicated GitHub page.

  • ODTbrain (docs/sources): Python library for diffraction tomography with the Born and Rytov approximations.
  • FDTD_sinogram (sources): C++ scripts and Python wrappers for diffraction-tomographic sinogram generation using MEEP.


Quantitative phase imaging (QPI)

QPI is an important part of any ODT analysis. The recorded data must be loaded and background-corrected before they are any use to a tomographic reconstruction algorithm. We have developed several Python packages that perform these preprocessing steps and enable additional analysis pipelines. For a full list, please visit our dedicated GitHub page.

  • DryMass (docs/sources): Manipulation and analysis of phase microscopy data. This Python package serves as a user-convenient command-line interface to all of the libraries listed below.
  • qpimage (docs/sources): Python library for manipulating quantitative phase images.
  • qpformat (docs/sources): Python library for opening quantitative phase imaging file formats.
  • qpsphere (docs/sources): Python library for spheres in quantitative phase imaging.


Real-time deformability cytometry (RT-DC)

In collaboration with Zellmechanik Dresden, we are developing the RT-DC analysis software Shape-Out.

  • Shape-Out 2 (docs/sources): Graphical application forthe  analysis and visualization of RT-DC data sets.
  • DCKit (sources): Graphical application for managing RT-DC data.
  • dclab (docs/sources): Python library for the post-measurement analysis of RT-DC data sets.


Other software

  • ggf (docs/sources): Python library for computing global geometric factors and corresponding stresses of the optical stretcher.

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