Modeling and Statistical analysis of fMRI data in Python
Project description
Nistats is being retired.
This repository is now archived.
It will not receive any further development or bug fixes. Its functionality has now been incorporated into Nilearn’s stats, datasets, and reporting modules.
It will be available in Nilearn 0.7.0 onwards, set to release in late 2020. Please file issues and pull requests in Nilearn, from now on.
Credit for the various Pull Requests that have been merged into Nistats are now visible in Nilearn. Open issues have been moved into Nilearn. Open PRs will need to be redone.
Nistats
Nistats is a Python module for fast and easy modeling and statistical analysis of functional Magnetic Resonance Imaging data.
It leverages the nilearn Python toolbox for neuroimaging data manipulation (data downloading, visualization, masking).
This work is made available by a community of people, amongst which the INRIA Parietal Project Team and D’esposito lab at Berkeley.
It is based on developments initiated in the nipy nipy project.
Important links
Official source code repo: https://github.com/nistats/nistats/
HTML documentation (stable release): http://nistats.github.io/
Dependencies
The required dependencies to use the software are:
Python >= 2.7
setuptools
Numpy >= 1.11
SciPy >= 0.17
Nibabel >= 2.0.2
Nilearn >= 0.4.0
Pandas >= 0.18.0
Sklearn >= 0.18.0
If you are using nilearn plotting functionalities or running the examples, matplotlib >= 1.5.1 is required.
If you want to run the tests, you need nose >= 1.2.1 and coverage >= 3.7.
If you want to download openneuro datasets Boto3 >= 1.2 is required
Install
The installation instructions are found on the webpage: https://nistats.github.io/
Development
Code
GIT
You can check the latest sources with the command:
git clone git://github.com/nistats/nistats
or if you have write privileges:
git clone git@github.com:nistats/nistats
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 nistats-0.0.1rc0.tar.gz
.
File metadata
- Download URL: nistats-0.0.1rc0.tar.gz
- Upload date:
- Size: 102.5 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.23.0 setuptools/46.0.0.post20200309 requests-toolbelt/0.9.1 tqdm/4.44.1 CPython/3.6.10
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | dcc4c4e410f542fd72e02e12b3b6531851bae2680d08ad29658b272587ef2f98 |
|
MD5 | f76b1a5c4766d8a55df56805fc55af4d |
|
BLAKE2b-256 | 02eb0da13a4906efb1f91060b3eef44f5e024649eff1e4d49678b7f02e82ef00 |
File details
Details for the file nistats-0.0.1rc0-py2.py3-none-any.whl
.
File metadata
- Download URL: nistats-0.0.1rc0-py2.py3-none-any.whl
- Upload date:
- Size: 121.6 kB
- Tags: Python 2, Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.23.0 setuptools/46.0.0.post20200309 requests-toolbelt/0.9.1 tqdm/4.44.1 CPython/3.6.10
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | e00be1810862e3a6a8dc81cc6563e8abca109e88ff7f4c2b96b29a769c1e599b |
|
MD5 | 24aa2eb316a22c89a707fbf8089ab908 |
|
BLAKE2b-256 | 5057034f882afce11ee6d61dd3c8e94fac543e5210470bb01fc5c066e1ae30b5 |