jupyter-book
hypothesis
jupyter-book | hypothesis | |
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15 | 20 | |
3,692 | 7,289 | |
0.8% | 0.9% | |
8.5 | 9.9 | |
6 days ago | 1 day ago | |
Python | Python | |
BSD 3-clause "New" or "Revised" License | GNU General Public License v3.0 or later |
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.
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?
hypothesis
- Hypothesis
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A Tale of Two Kitchens - Hypermodernizing Your Python Code Base
Hypothesis for Property-Based Testing: Hypothesis is a Python library facilitating property-based testing. It offers a distinct advantage by generating a wide array of input data based on specified properties or invariants within the code. The perks of Hypothesis include:
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Pix2tex: Using a ViT to convert images of equations into LaTeX code
But then add tests! Tests for LaTeX equations that had never been executable as code.
https://github.com/HypothesisWorks/hypothesis :
> Hypothesis is a family of testing libraries which let you write tests parametrized by a source of examples. A Hypothesis implementation then generates simple and comprehensible examples that make your tests fail. This simplifies writing your tests and makes them more powerful at the same time, by letting software automate the boring bits and do them to a higher standard than a human would, freeing you to focus on the higher level test logic.
> This sort of testing is often called "property-based testing", and the most widely known implementation of the concept is the Haskell library QuickCheck, but Hypothesis differs significantly from QuickCheck and is designed to fit idiomatically and easily into existing styles of testing that you are used to, with absolutely no familiarity with Haskell or functional programming needed.
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pgregory.net/rapid v1.0.0, modern Go property-based testing library
pgregory.net/rapid is a modern Go property-based testing library initially inspired by the power and convenience of Python's Hypothesis.
- Was muss man als nicht-technischer Quereinsteiger in Data Science *wirklich* können?
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Python toolkits
Hypothesis to generate dummy data for test.
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Best way to test GraphQL API using Python?
To create your own test cases, I recommend you use hypothesis-graphql in combination with hypothesis. hypothesis is a property-based testing library. Property-based testing is an approach to testing in which you make assertions about the result of a test given certain conditions and parameters. For example, if you have a mutation that requires a boolean parameter, you can assert that the client will receive an error if it sends a different type. hypothesis-graphql is a GraphQL testing library that knows how to use hypothesis strategies to generate query documents.
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Fuzzcheck (a structure-aware Rust fuzzer)
The Hypothesis stateful testing code is somewhat self-contained, since it mostly builds on top of internal APIs that already existed.
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Running C unit tests with pytest
We've had a lot of success combining that approach with property-based testing (https://github.com/HypothesisWorks/hypothesis) for the query engine at backtrace: https://engineering.backtrace.io/2020-03-11-how-hard-is-it-t... .
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Machine Readable Specifications at Scale
Systems I've used for this include https://agda.readthedocs.io/en/v2.6.0.1/getting-started/what... https://coq.inria.fr https://www.idris-lang.org and https://isabelle.in.tum.de
An easier alternative is to try disproving the statement, by executing it on thousands of examples and seeing if any fail. That gives us less confidence than a full proof, but can still be better than traditional "there exists" tests. This is called property checking or property-based testing. Systems I've used for this include https://hypothesis.works https://hackage.haskell.org/package/QuickCheck https://scalacheck.org and https://jsverify.github.io
What are some alternatives?
Spyder - Official repository for Spyder - The Scientific Python Development Environment
pytest - The pytest framework makes it easy to write small tests, yet scales to support complex functional testing
sphinx-thebe - A Sphinx extension to convert static code into interactive code cells with Jupyter, Thebe, and Binder.
Robot Framework - Generic automation framework for acceptance testing and RPA
MyST-Parser - An extended commonmark compliant parser, with bridges to docutils/sphinx
Behave - BDD, Python style.
quarto-cli - Open-source scientific and technical publishing system built on Pandoc.
nose2 - The successor to nose, based on unittest2
pre-commit - A framework for managing and maintaining multi-language pre-commit hooks.
nose - nose is nicer testing for python
heron
Schemathesis - Automate your API Testing: catch crashes, validate specs, and save time