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maint: conclude migration poldracklab -> nipy
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.github/workflows/travis.yml

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jobs:
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build-linux:
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if: "!contains(github.event.head_commit.message, '[skip ci]' && (github.event_name == 'push' || github.event.pull_request.head.repo.full_name != 'poldracklab/nitransforms'))"
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if: "!contains(github.event.head_commit.message, '[skip ci]' && (github.event_name == 'push' || github.event.pull_request.head.repo.full_name != 'nipy/nitransforms'))"
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runs-on: ubuntu-latest
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env:
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TEST_DATA_HOME: /home/runner/testdata/nitransforms-tests

CONTRIBUTING.rst

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involved. If you have any questions that aren’t discussed in our
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documentation, or it’s difficult to find what you’re looking for, please
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let us know by opening an
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`issue <https://github.com/poldracklab/fmriprep/issues>`__!
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`issue <https://github.com/nipy/nitransforms/issues>`__!

Dockerfile

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ARG VERSION
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LABEL org.label-schema.build-date=$BUILD_DATE \
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org.label-schema.name="nitransforms" \
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org.label-schema.vcs-url="https://github.com/poldracklab/nitransforms" \
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org.label-schema.vcs-url="https://github.com/nipy/nitransforms" \
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org.label-schema.version=$VERSION \
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org.label-schema.schema-version="1.0"

README.md

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# NiTransforms
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[![DOI](https://joss.theoj.org/papers/10.21105/joss.03459/status.svg)](https://doi.org/10.21105/joss.03459)
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[![ISBI2020](https://img.shields.io/badge/doi-10.31219%2Fosf.io%2F8aq7b-blue.svg)](https://doi.org/10.31219/osf.io/8aq7b)
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[![Deps & CI](https://github.com/poldracklab/nitransforms/actions/workflows/travis.yml/badge.svg)](https://github.com/poldracklab/nitransforms/actions/workflows/travis.yml)
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[![CircleCI](https://circleci.com/gh/poldracklab/nitransforms.svg?style=svg)](https://circleci.com/gh/poldracklab/nitransforms)
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[![codecov](https://codecov.io/gh/poldracklab/nitransforms/branch/master/graph/badge.svg)](https://codecov.io/gh/poldracklab/nitransforms)
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[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/poldracklab/nitransforms/master?filepath=docs%2Fnotebooks%2F)
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[![Deps & CI](https://github.com/nipy/nitransforms/actions/workflows/travis.yml/badge.svg)](https://github.com/nipy/nitransforms/actions/workflows/travis.yml)
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[![CircleCI](https://circleci.com/gh/nipy/nitransforms.svg?style=svg)](https://circleci.com/gh/nipy/nitransforms)
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[![codecov](https://codecov.io/gh/nipy/nitransforms/branch/master/graph/badge.svg)](https://codecov.io/gh/nipy/nitransforms)
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[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/nipy/nitransforms/master?filepath=docs%2Fnotebooks%2F)
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[![Docs](https://readthedocs.org/projects/nitransforms/badge/?version=latest)](http://nitransforms.readthedocs.io/en/latest/?badge=latest)
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A development repo for [nipy/nibabel#656](https://github.com/nipy/nibabel/pull/656)

docs/conf.py

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<div class="admonition note">
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This page was generated from
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<a class="reference external" href="https://github.com/poldracklab/nitransforms/blob/master\
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<a class="reference external" href="https://github.com/nipy/nitransforms/blob/master\
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/{{ docname|e }}">{{ docname|e }}</a>.
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<script>
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if (document.location.host) {

docs/index.rst

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:target: https://pypi.python.org/pypi/nitransforms/
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:alt: Latest Version
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.. image:: https://codecov.io/gh/poldracklab/nitransforms/branch/master/graph/badge.svg
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:target: https://codecov.io/gh/poldracklab/nitransforms
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.. image:: https://codecov.io/gh/nipy/nitransforms/branch/master/graph/badge.svg
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:target: https://codecov.io/gh/nipy/nitransforms
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.. image:: https://circleci.com/gh/poldracklab/nitransforms.svg?style=svg
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:target: https://circleci.com/gh/poldracklab/nitransforms
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.. image:: https://circleci.com/gh/nipy/nitransforms.svg?style=svg
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:target: https://circleci.com/gh/nipy/nitransforms
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.. image:: https://github.com/poldracklab/nitransforms/actions/workflows/travis.yml/badge.svg
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:target: https://github.com/poldracklab/nitransforms/actions
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.. image:: https://github.com/nipy/nitransforms/actions/workflows/travis.yml/badge.svg
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:target: https://github.com/nipy/nitransforms/actions
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2020
.. image:: https://img.shields.io/badge/doi-10.31219%2Fosf.io%2F8aq7b-blue.svg
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:target: https://doi.org/10.31219/osf.io/8aq7b
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.. image:: https://mybinder.org/badge_logo.svg
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:target: https://mybinder.org/v2/gh/poldracklab/nitransforms/master?filepath=docs%2Fnotebooks%2F
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:target: https://mybinder.org/v2/gh/nipy/nitransforms/master?filepath=docs%2Fnotebooks%2F
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About
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-----

docs/installation.rst

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Alternatively, you can install the bleeding-edge version of the software
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directly from the GitHub repo with::
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python -m pip install git+https://github.com/poldracklab/nitransforms.git@master
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python -m pip install git+https://github.com/nipy/nitransforms.git@master
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To verify the installation, you can run the following command::
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Advanced users and developers who plan to contribute with bugfixes, documentation,
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etc. can first clone our Git repository::
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git clone https://github.com/poldracklab/nitransforms.git
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git clone https://github.com/nipy/nitransforms.git
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and install the tool in *editable* mode::

docs/notebooks/isbi2020.ipynb

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"source": [
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"### Introduction\n",
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"#### Submission (654) Software tool to read, represent, manipulate and apply $n$-dimensional spatial transforms\n",
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"Spatial transforms formalize mappings between coordinates of objects in biomedical images. Transforms typically are the outcome of image registration methodologies, which estimate the alignment between two images. Image registration is a prominent task present in nearly all standard image processing and analysis pipelines. The proliferation of software implementations of image registration methodologies has resulted in a spread of data structures and file formats used to preserve and communicate transforms. This segregation of formats hinders the compatibility between tools and endangers the reproducibility of results. We propose a software tool capable of converting between formats and resampling images to apply transforms generated by the most popular neuroimaging packages and libraries (AFNI, FSL, FreeSurfer, ITK, and SPM). The proposed software is subject to continuous integration tests to check the compatibility with each supported tool after every change to the [code base](https://github.com/poldracklab/nitransforms). Compatibility between software tools and imaging formats is a necessary bridge to ensure the reproducibility of results and enable the optimization and evaluation of current image processing and analysis workflows.\n",
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"Spatial transforms formalize mappings between coordinates of objects in biomedical images. Transforms typically are the outcome of image registration methodologies, which estimate the alignment between two images. Image registration is a prominent task present in nearly all standard image processing and analysis pipelines. The proliferation of software implementations of image registration methodologies has resulted in a spread of data structures and file formats used to preserve and communicate transforms. This segregation of formats hinders the compatibility between tools and endangers the reproducibility of results. We propose a software tool capable of converting between formats and resampling images to apply transforms generated by the most popular neuroimaging packages and libraries (AFNI, FSL, FreeSurfer, ITK, and SPM). The proposed software is subject to continuous integration tests to check the compatibility with each supported tool after every change to the [code base](https://github.com/nipy/nitransforms). Compatibility between software tools and imaging formats is a necessary bridge to ensure the reproducibility of results and enable the optimization and evaluation of current image processing and analysis workflows.\n",
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"\n",
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"The process is summarized in the following figure:\n",
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"![alt text](../_static/figure1-joss.png \"Figure 1\")"
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"```Python\n",
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"pip install nitransforms\n",
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"```\n",
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"See also: https://github.com/poldracklab/nitransforms"
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"See also: https://github.com/nipy/nitransforms"
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]
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},
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{

joss/paper.md

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Therefore, "applying a transform" entails two operations (\autoref{fig:resampling}): first, transforming the coordinates of the samples in the reference image $R$ to find their mapping $\vec{x}'$ on $M$ via $T\{\cdot\}$, and second an interpolation step, as $\vec{x}'$ will likely fall off-the-grid of the moving image $M$.
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These two operations are confusing because, while the spatial transformation projects from $R$ to $M$, the data flows in reversed way after the interpolation of the values of $M$ at the mapped coordinates $\vec{x}'$.
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![Resampling a 3D image via a spatial transform to fuse the information of one into another image. \label{fig:resampling}](https://github.com/poldracklab/nitransforms/raw/master/docs/_static/figure1-joss.png)
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![Resampling a 3D image via a spatial transform to fuse the information of one into another image. \label{fig:resampling}](https://github.com/nipy/nitransforms/raw/master/docs/_static/figure1-joss.png)
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## Software Architecture
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setup.cfg

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project_urls =
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Manuscript=https://doi.org/10.31219/osf.io/8aq7b
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NiBabel=https://github.com/nipy/nibabel/pull/656
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url = https://github.com/poldracklab/nitransforms
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url = https://github.com/nipy/nitransforms
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[options]
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python_requires = >= 3.7

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