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.