Neuroimaging in Python: Pipelines and Interfaces
Project description
========================================================
NIPYPE: Neuroimaging in Python: Pipelines and Interfaces
========================================================
Current neuroimaging software offer users an incredible opportunity to analyze data using a variety of different algorithms. However, this has resulted in a heterogeneous collection of specialized applications without transparent interoperability or a uniform operating interface.
*Nipype*, an open-source, community-developed initiative under the umbrella of NiPy_, is a Python project that provides a uniform interface to existing neuroimaging software and facilitates interaction between these packages within a single workflow. Nipype provides an environment that encourages interactive exploration of algorithms from different packages (e.g., AFNI, ANTS, BRAINS, BrainSuite, Camino, FreeSurfer, FSL, MNE, MRtrix, MNE, Nipy, Slicer, SPM), eases the design of workflows within and between packages, and reduces the learning curve necessary to use different packages. Nipype is creating a collaborative platform for neuroimaging software development in a high-level language and addressing limitations of existing pipeline systems.
*Nipype* allows you to:
* easily interact with tools from different software packages
* combine processing steps from different software packages
* develop new workflows faster by reusing common steps from old ones
* process data faster by running it in parallel on many cores/machines
* make your research easily reproducible
* share your processing workflows with the community
NIPYPE: Neuroimaging in Python: Pipelines and Interfaces
========================================================
Current neuroimaging software offer users an incredible opportunity to analyze data using a variety of different algorithms. However, this has resulted in a heterogeneous collection of specialized applications without transparent interoperability or a uniform operating interface.
*Nipype*, an open-source, community-developed initiative under the umbrella of NiPy_, is a Python project that provides a uniform interface to existing neuroimaging software and facilitates interaction between these packages within a single workflow. Nipype provides an environment that encourages interactive exploration of algorithms from different packages (e.g., AFNI, ANTS, BRAINS, BrainSuite, Camino, FreeSurfer, FSL, MNE, MRtrix, MNE, Nipy, Slicer, SPM), eases the design of workflows within and between packages, and reduces the learning curve necessary to use different packages. Nipype is creating a collaborative platform for neuroimaging software development in a high-level language and addressing limitations of existing pipeline systems.
*Nipype* allows you to:
* easily interact with tools from different software packages
* combine processing steps from different software packages
* develop new workflows faster by reusing common steps from old ones
* process data faster by running it in parallel on many cores/machines
* make your research easily reproducible
* share your processing workflows with the community
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
nipype-1.0.1.tar.gz
(3.2 MB
view details)
Built Distribution
File details
Details for the file nipype-1.0.1.tar.gz.
File metadata
- Download URL: nipype-1.0.1.tar.gz
- Upload date:
- Size: 3.2 MB
- Tags: Source
- Uploaded using Trusted Publishing? No
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 47f62fda3d6b9a37aa407a6b78c80e91240aa71e61191ed00da68b02839fe258 |
|
| MD5 | dcbc85b7c90c3d354f7c1df964471541 |
|
| BLAKE2b-256 | 2ba8a21aa79bec83f142bbdbd67a456de2a676e597f1fd69530de2fa09fa8191 |
Provenance
File details
Details for the file nipype-1.0.1-py2.py3-none-any.whl.
File metadata
- Download URL: nipype-1.0.1-py2.py3-none-any.whl
- Upload date:
- Size: 3.2 MB
- Tags: Python 2, Python 3
- Uploaded using Trusted Publishing? No
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 9e67eac5f4190f664f971d80a8962e0bdf24113fba4f0e1a6e13f17f1c08c511 |
|
| MD5 | 0691d2780d9e7b60a6857715758c7ee1 |
|
| BLAKE2b-256 | 0716acd9d98787d9e3268b5141f0abe3e5ed0ecaa94bc8a0fe88ca4fcc0eb418 |
