============ Installation ============ ``Heudiconv`` is packaged and available from many different sources. .. _install_local: 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 `_. .. _install_container: Containers ========== Our container image releases are availe 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. 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.