Update

[ghstack-poisoned]

.dockerignore

@@ -1,6 +1,5 @@
-.github
-.vscode
-binder
-examples
-docs
-tests
+.github
+.vscode
+examples
+docs
+tests

.github/workflows/docs.yml

@@ -106,6 +106,24 @@ jobs:
         run: |
           echo "current jupyter-notebook: ${{ matrix.notebook }}"
 
+      - name: Add nb-myst download badge
+        run: |
+          notebook=${{ matrix.notebook }}
+          notebook_name=$(basename $notebook)
+          download_badge_md="[![Download notebook](https://img.shields.io/badge/Download-notebook-blue?logo=jupyter)](path:$notebook_name)"
+          python_command="import nbformat as nbf\n\
+          nb = nbf.read(open('$notebook'), as_version=4)\n\
+          # if the 1st cell is md and has colab text => add space after\n\
+          if nb['cells'][0]['cell_type'] == 'markdown' and 'colab' in nb['cells'][0]['source'].lower():\n\
+              nb['cells'][0]['source'] += ' '\n\
+          # if there is no md cell with colab => create empty md cell on top\n\
+          else:\n\
+              nb['cells'].insert(0, nbf.v4.new_markdown_cell())\n\
+          nb['cells'][0]['source'] += '$download_badge_md'\n\
+          nbf.write(nb, open('$notebook', 'w'))"
+
+          python -c "exec (\"$python_command\")"
+
       - name: Run notebook
         uses: fzimmermann89/run-notebook@v3
         env:

CITATION.cff

@@ -0,0 +1,55 @@
+abstract:
+    <p>MRpro is a MR image reconstruction and processing framework specifically
+    developed to work well with PyTorch. The data classes utilize torch tensors for
+    storing data such as MR raw data or reconstructed image data. Where possible batch
+    parallelisation of PyTorch is utilized to speed up image reconstruction.</p>
+authors:
+    - affiliation: Physikalisch-Technische Bundesanstalt
+      family-names: Zimmermann
+      given-names: Felix Frederik
+      orcid: 0000-0002-0862-8973
+    - affiliation: Physikalisch-Technische Bundesanstalt
+      family-names: Schuenke
+      given-names: Patrick
+      orcid: 0000-0002-3179-4830
+    - affiliation: Physikalisch-Technische Bundesanstalt
+      family-names: Brahma
+      given-names: Sherine
+      orcid: 0000-0003-4340-6513
+    - affiliation: Physikalisch-Technische Bundesanstalt
+      family-names: Guastini
+      given-names: Mara
+    - affiliation: Physikalisch-Technische Bundesanstalt
+      family-names: Hammacher
+      given-names: Johannes
+    - affiliation: Physikalisch-Technische Bundesanstalt
+      family-names: Kofler
+      given-names: Andreas
+      orcid: 0000-0002-7416-4433
+    - affiliation: Physikalisch-Technische Bundesanstalt
+      family-names: Kranich Redshaw
+      given-names: Catarina
+    - affiliation: Physikalisch-Technische Bundesanstalt
+      family-names: Lunin
+      given-names: Leonid
+      orcid: 0000-0001-6469-5532
+    - affiliation: Physikalisch-Technische Bundesanstalt
+      family-names: Martin
+      given-names: Stefan
+    - affiliation: Physikalisch-Technische Bundesanstalt
+      family-names: Schote
+      given-names: David
+      orcid: 0000-0003-3468-0676
+    - affiliation: Physikalisch-Technische Bundesanstalt
+      family-names: Kolbitsch
+      given-names: Christoph
+      orcid: 0000-0002-4355-8368
+cff-version: 1.2.0
+date-released: "2024-12-17"
+doi: 10.5281/zenodo.14509598
+license:
+    - apache-2.0
+repository-code: https://github.com/PTB-MR/mrpro/
+title: MRpro - PyTorch-based MR image reconstruction and processing package
+type: software
+url: https://doi.org/10.5281/zenodo.14509598

README.md

@@ -5,13 +5,14 @@
 ![Python](https://img.shields.io/badge/python-3.10%20%7C%203.11%20%7C%203.12-blue)
 [![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
 ![Coverage Bagde](https://img.shields.io/endpoint?url=https://gist.githubusercontent.com/ckolbPTB/48e334a10caf60e6708d7c712e56d241/raw/coverage.json)
+[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.14509598.svg)](https://doi.org/10.5281/zenodo.14509598)
 
 MR image reconstruction and processing package specifically developed for PyTorch.
 
 - **Source code:** <https://github.com/PTB-MR/mrpro>
 - **Documentation:** <https://ptb-mr.github.io/mrpro/>
 - **Bug reports:** <https://github.com/PTB-MR/mrpro/issues>
-- **Try it out:** [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/PTB-MR/mrpro.git/main?labpath=examples)
+- **Try it out:** [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/PTB-MR/mrpro)
 
 ## Awards
 

docs/source/contributor_guide.rst

@@ -5,8 +5,6 @@ Contributor Guide
 Repo structure
 ==============
 This repository uses a *pyproject.toml* file to specify all the requirements.
-If you need a "normal" *requirements.txt* file, please have a look in *binder*. There you find a *requirements.txt*
-automatically created from *pyproject.toml* using GitHub actions.
 
 **.github/workflows**
     Definitions of GitHub action workflows to carry out formatting checks, run tests and automatically create this
@@ -18,12 +16,9 @@ automatically created from *pyproject.toml* using GitHub actions.
 **.docs**
     Files to create this documentation.
 
-**binder**
-    *environment.yml* to install MRpro when starting a binder session.
-
 **examples**
     Python scripts showcasing how MRpro can be used. Any data needed has to be available from
-    an online repository (e.g. zenodo) such that it can be automatically downloaded. The scripts 
+    an online repository (e.g. zenodo) such that it can be automatically downloaded. The scripts
     are automatically translated to jupyter notebooks using GitHub
     actions. Individual cells should be indicated with ``# %%``. For markdown cells use ``# %% [markdown]``.
     The translation from python script to jupyter notebook is done using
@@ -67,14 +62,14 @@ src/mrpro structure
 
 Linting
 =======
-We use Ruff for linting. If you are using VSCode, you can install an 
-`extension <https://marketplace.visualstudio.com/items?itemName=charliermarsh.ruff>`_, 
-which we have also added to the list of extensions that VSCode should recommend when you open the code. 
+We use Ruff for linting. If you are using VSCode, you can install an
+`extension <https://marketplace.visualstudio.com/items?itemName=charliermarsh.ruff>`_,
+which we have also added to the list of extensions that VSCode should recommend when you open the code.
 We also run `mypy <https://pypi.org/project/mypy/>`_ as a type checker.
 
-In CI, our linting is driven by `pre-commit <https://pre-commit.com/>`_. 
+In CI, our linting is driven by `pre-commit <https://pre-commit.com/>`_.
 If you install MRpro via ``pip install -e .[test]``, pre-commit will be installed in your python environment.
-You can either add pre-commit to your git pre-commit hooks, requiring it to pass before each commit (``pre-commit install``), 
+You can either add pre-commit to your git pre-commit hooks, requiring it to pass before each commit (``pre-commit install``),
 or run it manually using ``pre-commit run --all-files`` after making your changes, before requesting a PR review.
 
 Naming convention
@@ -115,4 +110,4 @@ We are still in pre-release mode and do not guarantee a stable API / strict sema
 Compatibility
 =============
 We aim to always be compatible with the latest stable pytorch release and the latest python version supported by pytorch. We are compatible with one previous python version.
-Our type hints will usually only be valid with the latest pytorch version.
+Our type hints will usually only be valid with the latest pytorch version.

docs/source/examples.rst

@@ -1,8 +1,10 @@
 Examples
 ========
+.. |colab-badge| image:: https://colab.research.google.com/assets/colab-badge.svg
+    :target: https://colab.research.google.com/github/PTB-MR/mrpro
 
 Notebooks with examples of how you can use MRpro.
-All of the notebooks can directly be run via binder or colab from the repo.
+Each notebook can be launched in Colab |colab-badge|
 
 .. toctree::
    :maxdepth: 1

docs/source/user_guide.rst

@@ -6,7 +6,7 @@ MRpro is a MR image reconstruction and processing framework specifically develop
 The data classes utilize torch tensors for storing data such as MR raw data or reconstructed image data.
 Where possible batch parallelisation of pytorch is utilized to speed up image reconstruction.
 
-MRpro is designed to work directly from MR raw data using the  `MRD <https://ismrmrd.readthedocs.io/en/latest/>`_ data format.
+MRpro is designed to work directly from MR raw data using the `MRD <https://ismrmrd.readthedocs.io/en/latest/>`_ data format.
 
 A basic pipeline would contain the following steps:
 
@@ -15,9 +15,13 @@ A basic pipeline would contain the following steps:
 * Data reconstruction
 * Image processing
 
+.. |colab-badge| image:: https://colab.research.google.com/assets/colab-badge.svg
+    :target: https://colab.research.google.com/github/PTB-MR/mrpro
+
 The following provides some basic information about these steps.
 For more detailed information please have a look at the notebooks in the *examples* folder.
-You can easily start a binder session via the badge in the *README* and give the notebooks a try without having to
+
+You can easily launch notebooks via the |colab-badge| badge and give the notebooks a try without having to
 install anything.
 
 Reading in raw data