ModelArrayIO is a Python package that converts between neuroimaging formats (fixel .mif, voxel NIfTI, CIFTI-2 dscalar) and the HDF5 (.h5) layout used by the R package ModelArray. It can also write ModelArray statistical results back to imaging formats.

Relationship to ConFixel: The earlier project ConFixel is superseded by ModelArrayIO. The ConFixel repository is retained for history (including links from publications) and will be archived; new work should use this repository.

Documentation for installation and usage: ModelArrayIO on GitHub (this README). For conda, HDF5 libraries, and installing the ModelArray R package, see the ModelArray vignette Installation.

ModelArrayIO provides three converter areas, each with import and export commands:

Once ModelArrayIO is installed, these commands are available in your terminal:

• Fixel-wise data (MRtrix .mif):
  • .mif → .h5: confixel (CLI name kept for compatibility with earlier ConFixel workflows)
  • .h5 → .mif: fixelstats_write
• Voxel-wise data (NIfTI):
  • NIfTI → .h5: convoxel
  • .h5 → NIfTI: volumestats_write
• Greyordinate-wise data (CIFTI-2):
  • CIFTI-2 → .h5: concifti
  • .h5 → CIFTI-2: ciftistats_write

For fixel-wise .mif conversion, the confixel / fixelstats_write tools use MRtrix mrconvert. Install MRtrix from MRtrix’s webpage if needed. Run mrview in the terminal to verify the installation.

If your data are voxel-wise or CIFTI only, you can skip this step.

You may want a conda environment first—see ModelArray: Installation. If MRtrix is installed in that environment, install ModelArrayIO in the same environment.

Install from GitHub:

git clone https://github.com/PennLINC/ModelArrayIO.git
cd ModelArrayIO
pip install .   # build via pyproject.toml

Editable install for development:

# From the repository root
pip install -e .

With hatch installed, you can build wheels/sdist locally:

hatch build
pip install dist/*.whl

We provide a walkthrough for fixel-wise data (confixel / fixelstats_write) and a walkthrough for voxel-wise data (convoxel / volumestats_write).

Together with ModelArray, see the combined walkthrough with example fixel-wise data (ModelArray + ModelArrayIO).

CLI help:

confixel --help

Use the same pattern for convoxel, concifti, fixelstats_write, volumestats_write, and ciftistats_write.

ModelArrayIO supports two on-disk backends for the subject-by-element matrix:

• HDF5 (default), implemented in modelarrayio/h5_storage.py
• TileDB, implemented in modelarrayio/tiledb_storage.py

Both backends expose a similar API:

• create a dense 2D array (subjects, items) and write all values at once
• create an empty array with the same shape and write by column stripes
• write/read column names alongside the data

Notes and minor differences:

• Chunking vs tiling: HDF5 uses chunks; TileDB uses tiles. We compute tile sizes analogous to chunk sizes to keep write/read patterns similar.
• Compression: HDF5 uses gzip by default; TileDB defaults to zstd with shuffle for better speed/ratio. You can switch to gzip for parity.
• Metadata: HDF5 stores column_names as a dataset attribute; TileDB stores names as JSON metadata on the array/group.
• Layout: Both backends keep dimensions in the same order and use zero-based indices.