ITK
jupyter-book
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ITK | jupyter-book | |
---|---|---|
7 | 15 | |
1,337 | 3,678 | |
1.6% | 1.2% | |
9.8 | 8.6 | |
2 days ago | 11 days ago | |
C++ | Python | |
Apache License 2.0 | BSD 3-clause "New" or "Revised" License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
ITK
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Create Elegant C++ Spatial Processing Pipelines in WebAssembly
The itkImage.h header is ITK's standard n-dimensional image data structure.
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Welcome and guide first-time contributors with a GitHub Action
In this post, we review how the Insight Toolkit (ITK) leverages the first-interaction GitHub Action to communicate our appreciation of the efforts of first-time contributors, establish norms for behavior, and provide civil pointers on where to find more information.
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How to raise the quality of scientific Jupyter notebooks
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.
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Holy shit, it really seems to be working!
It also depends heavily on the toolchain. One of the first successful toolkits used to circumvent image-based security measures was ITK, originally a toolkit for medical image processing. That's not even using AI (at least back then). Here you build "piplines" by lego'ing together functions like building blocks, there are rules to it, but the sleek interface design make it very versatile. It was a nightmare to devise ways to counteract, since the crucial processing steps could easily be switched around as long as the linear algebra made sense. And when you have a toolchain excelling in fourier-space based analysis and interaction, the linear algebra makes sense in a lot of different orders of doing steps.
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Ask HN: What is a cool technology to learn?
Yeah, Prolog is pretty cool!
Another technology I found interesting too learn is ITK (https://itk.org/). You need a different mindset using ITK than other image processing libraries.
Lisp is cool as well.
And fully homomorphic encryption.
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Give me a starting nudge: microscopy image processing in python
VTK is a powerful visualization package, but it's more for working with 3D FEM/CFD data. There are lots of things in there though, so it may be useful. The sister project Paraview is an application which can be used to work with data interactively. Both have great Python support. There is also ITK which is focused on with image data, like medical scans - never used it, though.
jupyter-book
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I don't always use LaTeX, but when I do, I compile to HTML (2013)
Sphinx supports ReStructuredText and Markdown.
MyST-Markdown supports MathJaX and Sphinx roles and directives. https://myst-parser.readthedocs.io/en/latest/
jupyter-book supports ReStructuredText, Jupyter Notebooks, and MyST-Markdown documents:
You can build Sphinx and Jupyter-Book projects with the ReadTheDocs container, which already has LaTeX installed: https://github.com/executablebooks/jupyter-book/issues/991
myst-templates/plain_latex_book:
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Ask HN: Fastest way to turn a Jupyter notebook into a website these days?
your task is very very broad
you mention you don't want to deal with AWS, if it's because of ad-hoc installation concerns and nothing else you can just run your notebooks in ready-made solutions like Google Colab, or Jupyter-book in Github ( https://github.com/executablebooks/jupyter-book ))
that would cover a lot of use cases right away without next to no learning curve
If you don't want to deal with AWS or similar, in that case:
- if it's a static notebook then you can obviously render it and serve the web content (might seem obvious but needs to be considered)
- if it's dynamic but has light hardware requirements, you can try jupyterlite which runs in the browser and should do a pyodine (webassembly CPython kernel) can do: https://jupyterlite.readthedocs.io/en/latest/try/lab/
- otherwise, you can try exposing a dockerised jupyter env ( as in https://github.com/MKAbuMattar/dockerized-jupyter-notebook/b... ) or even better a nixified one ( https://github.com/tweag/jupyenv )
there might be other approaches I'm missing, but I think that's pretty much it that doesn't entail some proprietary solution or an ad-hoc installation as you've been doing
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How to raise the quality of scientific Jupyter notebooks
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.
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Sunday Daily Thread: What's everyone working on this week?
See this thread for example.
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Are there any frameworks/methodologies/libraries that can help to create a PDF printable professionally looking written report?
And maybe take a look at executablebooks/jupyter-book.
- [P] I Made An Easy-To-Use Python Package That Creates Beautiful Html Reports From Jupyter Notebooks
- RStudio Is Becoming Posit
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Python toolkits
Our team has transferred from Sphinx for documentation to JupyterBook. There have been some growing pains with it but I prefer the look of the output and being able to play with the examples on Colab or Binder at the click of a button is a great feature.
- Ask HN: Tools to generate coverage of user documentation for code
- Why does [::-1] reverse a list?
What are some alternatives?
OpenCV - Open Source Computer Vision Library
Spyder - Official repository for Spyder - The Scientific Python Development Environment
VTK - Mirror of Visualization Toolkit repository
sphinx-thebe - A Sphinx extension to convert static code into interactive code cells with Jupyter, Thebe, and Binder.
GDCM - Grassroots DICOM read-only mirror. Only for Pull Request. Please report bug at http://sf.net/p/gdcm
MyST-Parser - An extended commonmark compliant parser, with bridges to docutils/sphinx
tesseract-ocr - Tesseract Open Source OCR Engine (main repository)
quarto-cli - Open-source scientific and technical publishing system built on Pandoc.
CImg - The CImg Library is a small and open-source C++ toolkit for image processing
pre-commit - A framework for managing and maintaining multi-language pre-commit hooks.
Flutter - Flutter makes it easy and fast to build beautiful apps for mobile and beyond
heron