Heudiconv is packaged and available from many different sources.
Released versions of HeuDiConv are available on PyPI
and conda.
If installing through PyPI, eg:
pip install heudiconv[all]
Manual installation of dcm2niix
is required. You can also benefit from an installer/downloader helper dcm2niix package
on PyPI, so you can simply pip install dcm2niix if you are installing in user space so
subsequently it would be able to download and install dcm2niix binary.
On Debian-based systems, we recommend using NeuroDebian, which provides the heudiconv package.
Our container image releases are available on our Docker Hub
If Docker is available on your system, you can pull the latest release:
$ docker pull nipy/heudiconv:latest
Additionally, HeuDiConv is available through the Docker image at repronim/reproin provided by
ReproIn heuristic project, which develops the reproin heuristic.
To maintain provenance, it is recommended that you use the latest tag only when testing out heudiconv.
Otherwise, it is recommended that you use an explicit version and record that information alongside the produced data.
If Singularity is available on your system, you can use it to pull and convert our Docker images! For example, to pull and build the latest release, you can run:
$ singularity pull docker://nipy/heudiconv:latest
ReproNim provides a large collection of Singularity container images of popular
neuroimaging tools, e.g. all the BIDS-Apps. This collection also includes the forementioned container
images for HeuDiConv and
ReproIn in the Singularity image format. This collection is available as a
DataLad dataset at ///repronim/containers
on datasets.datalad.org and as a GitHub repo.
The HeuDiConv and ReproIn container images are named nipy-heudiconv and repronim-reproin, respectively, in this collection.
To use them, you can install the DataLad dataset and then use the datalad containers-run command to run.
For a more detailed example of using images from this collection while fulfilling
the YODA Principles, please check out
A typical YODA workflow in
the documentation of this collection.
Note: With the datalad containers-run command, the images in this collection work on macOS (OSX)
as well for repronim/containers helpers automagically take care of running the Singularity containers via Docker.