This repository contains Python data processing scripts to be used with the UFO framework. At the moment they are targeted at high-performance reconstruction of tomographic data sets.

Run

python setup.py install

in a prepared virtualenv or as root for system-wide installation.

To do a tomographic reconstruction you simply call

$ tofu tomo --sinograms $PATH_TO_SINOGRAMS

from the command line. To get get correct results, you may need to append options such as --axis-pos/-a and --angle-step/-a (which are given in radians!). Input paths are either directories or glob patterns. Output paths are either directories or a format that contains one %i specifier:

$ tofu tomo --axis-pos=123.4 --angle-step=0.000123 \
     --sinograms="/foo/bar/*.tif" --output="/output/slices-%05i.tif"

You can get a help for all options by running

$ tofu tomo --help

and more verbose output by running with the -v/--verbose flag.

You can also load reconstruction parameters from a configuration file called reco.conf. You may create a template with

$ tofu init

Note, that options passed via the command line always override configuration parameters!

Besides scripted reconstructions, one can also run a standalone GUI for both reconstruction and quick assessment of the reconstructed data via

$ tofu gui

If you are running at least ufo-core/filters 0.6, you can evaluate the performance of the filtered backprojection (without sinogram transposition!), with

$ tofu perf

You can customize parameter scans, pretty easily via

$ tofu perf --width 256:8192:256 --height 512

which will reconstruct all combinations of width between 256 and 8192 with a step of 256 and a fixed height of 512 pixels.

If you do not know the correct center of rotation from your experimental setup, you can estimate it with:

$ tofu estimate -i $PATH_TO_SINOGRAMS

Currently, a modified algorithm based on the work of Donath et al. is used to determine the center.