Lightweight implementation of neuromaps
Project description
lytemaps
This repository contains an implementation of a minimal fork of neuromaps
functionality. If you're an end user, this is likely not for you -- this implementation exists mostly for hypercoil
developer purposes. As developers, we'd like to use certain neuromaps
operations without burdening our software with the bulky dependencies (nilearn
, sklearn
and Connectome Workbench) that the full install of neuromaps
requires. This implementation is intended to support the subset of functionality that requires only core Scientific Python packages together with the essential nibabel
and pooch
for handling data fetch operations. (But we're not there yet -- in particular, many transforms still require Workbench at this time.) Obviously, functionality here is limited -- inter alia, this means that null models aren't included for now. As development progresses, we'll index the neuromaps
functions that are implemented below.
If for some reason you still decide to use this repository, please follow the citation prescriptions from Neuromaps and pooch. In particular, specify that you used lytemaps
in your methods section, specify that lytemaps
comprises code from neuromaps
with a pooch
-based downloader backend, and cite the neuromaps
and pooch
papers.
License
Most code in this repository is taken directly from the neuromaps
repository, and is therefore licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License. The exception to this is several small utility functions in src/lytemaps/utils.py
and src/lytemaps/datasets/utils.py
, which are taken from the nilearn
and sklearn
repositories and are therefore licensed under the 3-clause BSD license. As we redevelop code into our own implementations, we will relicense. See the LICENSE
file for more details.
Roadmap
- Implement
pooch
-based data fetchers forneuromaps
datasets - Add support for token-based authentication for OSF datasets
- Add support for querying the
templateflow
API - Use
nitransforms
wherever possible for surface-to-surface, volume-to-surface, and surface-to-volume transforms - Use neuroimaging tensor library backend for operations originally implemented with
nilearn
andsklearn
Project details
Release history Release notifications | RSS feed
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
File details
Details for the file lytemaps-0.0.3.tar.gz
.
File metadata
- Download URL: lytemaps-0.0.3.tar.gz
- Upload date:
- Size: 73.5 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/5.0.0 CPython/3.10.4
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 1370570f5ca7d720d1680e26fae9a195b0f9459072bff6f52e3738ea548bb3b0 |
|
MD5 | 8ce19fb30b53cd504f5a5218c65a0841 |
|
BLAKE2b-256 | d8346a56cc1fed152725f111b4d042b8359d40ca657791fb99cc484189e9450f |
File details
Details for the file lytemaps-0.0.3-py3-none-any.whl
.
File metadata
- Download URL: lytemaps-0.0.3-py3-none-any.whl
- Upload date:
- Size: 74.6 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/5.0.0 CPython/3.10.4
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | fa0aa37e0e2d1bb1202ff29c920757c256a513d5a2bafa3b0ab3fea6d8bbf8b3 |
|
MD5 | 02bc2589268972ebfad7db0a612e77af |
|
BLAKE2b-256 | d5d8f7b3418fde8329e882be9e4e64aed690e425f5b9f143e328c8c1a31fb504 |