Installation¶
Heudiconv
is packaged and available from many different sources.
Local¶
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.
Docker¶
If Docker is available on your system, you can visit our page on Docker Hub to view available releases. To pull the latest release, run:
$ docker pull nipy/heudiconv:latest
Note that when using HeuDiConv via docker run
, you might need to provide your user and group IDs so they map correspondingly
within the container, i.e.:
$ docker run --user=$(id -u):$(id -g) -e "UID=$(id -u)" -e "GID=$(id -g)" --rm -t -v $PWD:$PWD nipy/heudiconv:latest [OPTIONS TO FOLLOW]
Additionally, HeuDiConv is available through the Docker image at repronim/reproin provided by
ReproIn heuristic project, which develops the reproin
heuristic.
Singularity¶
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
Singularity YODA style using ///repronim/containers¶
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.