hypothesis
yapf
hypothesis | yapf | |
---|---|---|
20 | 21 | |
7,289 | 13,655 | |
0.9% | 0.3% | |
9.9 | 8.0 | |
2 days ago | 5 days ago | |
Python | Python | |
GNU General Public License v3.0 or later | Apache License 2.0 |
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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.
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
yapf
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Enhance Your Project Quality with These Top Python Libraries
YAPF (Yet Another Python Formatter): YAPF takes a different approach in that it’s based off of ‘clang-format’, a popular formatter for C++ code. YAPF reformats Python code so that it conforms to the style guide and looks good.
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Why is Prettier rock solid?
I think I agree about the testing and labor of complicated translation rules.
But it doesn't appear that almost every pretty printer uses the Wadler pretty printing paper. It seems like MOST of them don't?
e.g. clang-format is one of the biggest and best, and it has a model that includes "unwrapped lines", a "layouter", a line break cost function, exhaustive search with memoization, and Dijikstra's algorithm:
https://llvm.org/devmtg/2013-04/jasper-slides.pdf
The YAPF Python formatter is based on this same algorithm - https://github.com/google/yapf
The Dart formatter used a model of "chunks, rules, and spans"
https://journal.stuffwithstuff.com/2015/09/08/the-hardest-pr...
It almost seems like there are 2 camps -- the functional algorithms for functional/expression-based languages, and other algorithms for more statement-based languages.
Though I guess Prettier/JavaScript falls on the functional side.
I just ran across this survey on lobste.rs and it seems to cover the functional pretty printing languages influenced by Wadler, but functional style, but not the other kind of formatter ("Google" formatters perhaps)
https://arxiv.org/pdf/2310.01530.pdf
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A Tale of Two Kitchens - Hypermodernizing Your Python Code Base
To get all your code into a consistent format the next step is to run a formatter. I recommend black, the well-known uncompromising code formatter, which is the most popular choice. Alternatives to black are autoflake, prettier and yapf, if you do not agree with blacks constraints.
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Front page news headline scraping data engineering project
Use yapf to format code -> https://github.com/google/yapf
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Confused by Google's docstring "Attributes" section.
Google is surprisingly rigorous when it comes to code formatting. I have been a software engineer at Amazon and it was nothing like what the book says happens at Google. So the conventions you see for python docstring formatting are primarily designed to integrate with Google's internal tooling. By using docstrings following the Google conventions, you will ultimately end up with automated documentation and other fancy automated things (like type checking which they did in the docstring before there were type hints). Also notably, Google has an open source python formatting tool that they use internally called YAPF (which stands for "Yet Another Python Formatter". So if you really want to go all-in on Google python style, grab that, too.
- Alternate python spacing.
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Not sure if this is the worst or most genius indentation I've seen
https://github.com/google/yapf has configs, do ctrl+f SPLIT_COMPLEX_COMPREHENSION in the readme
- Google Python Style Guide
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Enable hyphenation only for code blocks
Only as recommendation: If the lines of the source code (here: you C code you aim to document) are kept short, in manageable bytes (similar to entries parser.add_argument in Clark's "Tiny Python Projects", example seldomly pass beyond the frequently recommended threshold of 80 characters/line), reporting with listings becomes easier (equally, the reading of the difference logs/views by git and vimdiff), than with lines of say 120 characters per line. Though we no longer are constrained to 80 characters per line by terminals/screens and punch cards (when Fortran still was FORTRAN), this is a reason e.g., yapf for Python allows you to choose between 4 spaces/indentation (PEP8 style), or 2 spaces/indentation (Google style).
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3 popular Python style guides that will help your team write better code
There is also a formatter for Python files called yapf that your team can use to avoid arguing over formatting conventions. Plus, Google also provides a settings file for Vim, noting that the default settings should be enough if you're using Emacs.
What are some alternatives?
pytest - The pytest framework makes it easy to write small tests, yet scales to support complex functional testing
black - The uncompromising Python code formatter
Robot Framework - Generic automation framework for acceptance testing and RPA
isort - A Python utility / library to sort imports.
Behave - BDD, Python style.
flake8
nose2 - The successor to nose, based on unittest2
autopep8 - A tool that automatically formats Python code to conform to the PEP 8 style guide.
nose - nose is nicer testing for python
awesome-python-typing - Collection of awesome Python types, stubs, plugins, and tools to work with them.
Schemathesis - Automate your API Testing: catch crashes, validate specs, and save time
pyright - Static Type Checker for Python