This repository aims at centralising MATLAB/Octave tools to interact with datasets conforming to the BIDS (Brain Imaging Data Structure) format.
For more information about BIDS, visit https://bids.neuroimaging.io/.
Join our chat on the BIDS-MATLAB channel on the brainhack mattermost.
See also PyBIDS for Python and the BIDS Starter Kit.
bids.layout
),bids.query
),bids.report
)bids.util.jsondecode
and bids.util.jsonencode
) provided that the right dependencies are installed,bids.util.tsvread
and bids.util.tsvwrite
),The behavior of this toolbox assumes that it is interacting with a valid BIDS dataset that should have been validated using BIDS-validator. If the Node.js version of the validator is installed on your computer, you can call it from the matlab prompt using bids.validate
. Just be aware that any unvalidated components may produce undefined behavior. Although, if you're BIDS-y enough, the behavior may be predictable.
We are trying to centralize the requests for new features in this issue: have a browse to see what could be coming soon or if we have missed something obvious.
Download this repository and add it to your MATLAB/Octave path.
unzip('https://github.com/bids-standard/bids-matlab/archive/master.zip');
addpath('bids-matlab-master');
If your version of MATLAB/Octave does not support JSON natively, please also install SPM12 or JSONio.
BIDS = bids.layout('/home/data/ds000117');
bids.query(BIDS, 'subjects')
A tutorial is available as a Jupyter Notebook and can be run interactively via Binder.
BIDS-MATLAB works with:
We aim for compatibility with the latest stable release of Octave at any time. Compatibility can sometimes also be achieved with older versions of Octave but this is not guaranteed.
If you are using MATLAB R2016b or newer, nothing else needs to be installed.
If you are using MATLAB R2016a or older, or using Octave, you need to install a supported JSON library for your MATLAB or Octave. This can be any of:
Starting point was spm_BIDS.m
from SPM12 (documentation) reformatted in a +bids
package with dependencies to other SPM functions removed.
Thanks goes to these wonderful people (emoji key):
This project follows the all-contributors specification. Contributions of any kind welcome!
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本文参考如下: Instant Recognition with Caffe Extracting Features Caffe Python特征提取 caffe 练习4 —-利用python批量抽取caffe计算得到的特征——by 香蕉麦乐迪 caffe 练习3 用caffe提供的C++函数批量抽取图像特征——by 香蕉麦乐迪 caffe python批量抽取图像特征 caffe python