How to raise the quality of scientific Jupyter notebooks

This page summarizes the projects mentioned and recommended in the original post on dev.to

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  • nbmake

    ๐Ÿ“ Pytest plugin for testing notebooks

  • nbmake is a pytest plugin that enables developers to verify that Jupyter notebook files run without error. This lets teams keep notebook-based documents and research up to date in an evolving project.

  • nbval

    A py.test plugin to validate Jupyter notebooks

  • nbval is a strong fit for developers who want to check that cells always render to the same value

  • InfluxDB

    Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

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  • testbook

    ๐Ÿงช ๐Ÿ“— Unit test your Jupyter Notebooks the right way

  • testbook is maintained by the nteract community, and is good for testing functions written inside notebooks

  • pytest

    The pytest framework makes it easy to write small tests, yet scales to support complex functional testing

  • Since ITK's inception in 1999, there has been a focus on engineering practices that result in high-quality software. High-quality scientific software is driven by regression testing. The ITK project supported the development of CTest and CDash unit testing and software quality dashboard tools for use with the CMake build system. In the Python programming language, the pytest test driver helps developers write small, readable scripts that ensure their software will continue to work as expected. However, pytest can only test Python scripts by default, and errors in untested computational notebooks are more common than well-tested Python code.

  • ITKIOScanco

    ITK Image IO for Scanco MicroCT .ISQ files

  • In ITK extensions such as ITKIOScanco (a module to work with 3D microtomography volumes) or ITKElastix (a toolbox for rigid and nonrigid registration of images), nbmake runs notebooks in GitHub Actions CI tests.

  • ITKElastix

    An ITK Python interface to elastix, a toolbox for rigid and nonrigid registration of images

  • In ITK extensions such as ITKIOScanco (a module to work with 3D microtomography volumes) or ITKElastix (a toolbox for rigid and nonrigid registration of images), nbmake runs notebooks in GitHub Actions CI tests.

  • jupyter-book

    Create beautiful, publication-quality books and documents from computational content.

  • Note: If you want to present a cleaner version of the notebook without assertions, you can use Jupyter book to render it into a site and use the remove-cell tag to omit assertions from the output.

  • SaaSHub

    SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives

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  • ITK

    Insight Toolkit (ITK) -- Official Repository. ITK builds on a proven, spatially-oriented architecture for processing, segmentation, and registration of scientific images in two, three, or more dimensions.

  • Jupyter has emerged as a fundamental component in artificial intelligence (AI) solution development and scientific inquiry. Jupyter notebooks are prevelant in modern education, commercial applications, and academic research. The Insight Toolkit (ITK) is an open source, cross-platform toolkit for N-dimensional processing, segmentation, and registration used to obtain quantitative insights from medical, biomicroscopy, material science, and geoscience images. The ITK community highly values scientific reproducibility and software sustainability. As a result, advanced computational methods in the toolkit have a dramatically larger impact because they can be reproducibly applied in derived research or commercial applications.

  • starter-workflows

    Accelerating new GitHub Actions workflows

  • In ITK extensions such as ITKIOScanco (a module to work with 3D microtomography volumes) or ITKElastix (a toolbox for rigid and nonrigid registration of images), nbmake runs notebooks in GitHub Actions CI tests.

NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a more popular project.

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