ModelArrayIO is a Python package that converts between neuroimaging formats (fixel .mif, voxel NIfTI, CIFTI-2 dscalar/pscalar/pconn) 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Relationship to ConFixel: The earlier project ConFixel is superseded by ModelArrayIO.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:
Neuroimaging data (CIFTI, NIfTI, or MRtrix .mif):
Neuroimaging → .h5: modelarrayio to-modelarray
.h5 → Neuroimaging: modelarrayio export-results
Storage backends: HDF5 and TileDB
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.
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file modelarrayio-26.0.0.tar.gz.
File metadata
- Download URL: modelarrayio-26.0.0.tar.gz
- Upload date:
- Size: 1.5 MB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via:
twine/6.2.0 CPython/3.14.6
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
48b5ebca65135d9d2592686ac68069b99b73f8a6a00f6751630fcc793ac0711b
|
|
| MD5 |
8e86fc33d3a0d1c010b794ad225f8573
|
|
| BLAKE2b-256 |
ef5463c7170dad80f74aca33863373a9b293c5281921bb5b5ff48d213ff225dc
|
File details
Details for the file modelarrayio-26.0.0-py3-none-any.whl.
File metadata
- Download URL: modelarrayio-26.0.0-py3-none-any.whl
- Upload date:
- Size: 51.0 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via:
twine/6.2.0 CPython/3.14.6
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
a4c83beaa148463187bdd5c136330ffa8ce1dd4424135d68113bcbeba61ebe8f
|
|
| MD5 |
8209c61ed4911a59fe644750fb6f9f9d
|
|
| BLAKE2b-256 |
b25be909ecf4ada573cca2633061c3f6252c1cf925108eb8cb5cb9dd24157593
|
