Get started (quickly)¶
This should not take much time. testkraut contains no compiled code. It should run with Python 2.6 (or later) – although Python 3x hasn’t been tested (yet). If you are running Python 2.6 you should install the argparse package, otherwise you won’t have much fun. Here is a list of things the make life more interesting:
- NumPy
- not strictly required, but strongly recommended. There should be no need to have any particular version.
- SciPy
- will improve the test result reporting – any reasonably recent version should do
- libmagic
- helps to provide more meaningful information on file types
- python-colorama
- for more beautiful console output – but monochrome beings don’t need it
Download ...¶
testkraut is available from PyPi, hence it can be installed with
easy_install
or pip
– the usual way. pip
seems to be a little saner
than the other one, so we’ll use this:
% pip install testkraut
This should download and install the latest version. Depending on where you are
installing you might want to call sudo
for additional force.
pip
will tell you where it installed the main testkraut
script.
Depending on your setup you may want to add this location to your PATH
environment variable.
... and run¶
Now we’re ready to run our first test. The demo
test requires FSL to be
installed and configured to run (properly set FSLDIR
variable and so on...).
The main testkraut script supports a number of commands that are used to prepare
and run tests. A comprehensive listing is available form the help output:
% testkraut --help
To run the demo
test, we need to obtain the required test data first. This
is done by telling testkraut to cache all required files locally:
% testkraut cachefiles demo
It will download an anatomical image from a webserver. However, since the image is the MNI152 template head that comes with FSL, you can also use an existing local file to populate the cache – please explore the options for this command.
Now we are ready to run:
% testkraut execute demo
If FSL is functional, this command will run a few seconds and create a
subdirectory testbeds/demo
with the test in/output and a comprehensive
description of the test run in JSON format:
% ls testbeds/demo
brain_mask.nii.gz brain.nii.gz head.nii.gz spec.json
That is it – for now...