hypothesis
isort
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hypothesis | isort | |
---|---|---|
20 | 41 | |
7,254 | 6,306 | |
1.1% | 0.9% | |
9.9 | 7.7 | |
10 days ago | 4 days ago | |
Python | Python | |
GNU General Public License v3.0 or later | MIT License |
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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
isort
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Enhance Your Project Quality with These Top Python Libraries
isort: This library sorts your imports alphabetically, and automatically separates them into sections and by type. It provides a cleaner and more organised way to manage project imports.
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A Tale of Two Kitchens - Hypermodernizing Your Python Code Base
isort will sort the imports for you
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Django Code Formatting and Linting Made Easy: A Step-by-Step Pre-commit Hook Tutorial
isort is a Python utility that helps in sorting and organizing import statements in Python code to create readable and consistent code. It automatically formats import statements in accordance with PEP 8.
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How to Write Impeccably Clean Code That Will Save Your Sanity
repos: - repo: https://github.com/ambv/black rev: 23.3.0 hooks: - id: black args: [--config=./pyproject.toml] language_version: python3.11 - repo: https://github.com/pycqa/flake8 rev: 6.0.0 hooks: - id: flake8 args: [--config=./tox.ini] language_version: python3.11 - repo: https://github.com/pycqa/isort rev: 5.12.0 hooks: - id: isort args: ["--profile", "black", "--filter-files"] language_version: python3.11 - repo: https://github.com/pre-commit/pre-commit-hooks rev: v4.4.0 hooks: - id: requirements-txt-fixer language_version: python3.11 - id: debug-statements - id: detect-aws-credentials - id: detect-private-key
- Automate Python Linting and Code Style Enforcement with Ruff and GitHub Actions
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Improve your Django Code with pre-commit
repos: ... pre-commmit stuff ... black stuff - repo: https://github.com/pycqa/isort rev: 5.12.0 hooks: - id: isort name: isort (python)
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How I start every new Python backend API project
isort
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nbdev formating and linting
isort , A Python utility / library to sort imports.
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Curious what is too much on one line... how 'compressed' can our code be?
Install black and isort and just don't worry about it. :-)
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I wrote a script to periodically change my Desktop background to live satellite images!
Sure. Also, and don't take this the wrong way, but there are some code smells in your project that could be partially mitigated with some basic linting/formatting. I suggest black as a code formatter, flake8 for basic linting, and isort for sorting imports (for example, you have local imports mixed in with standard library and third party imports). You can install these via pip and most editors (like VS Code) can autoformat on save and show you linting problems as you edit. And you can integrate these into your workflow by using pre-commit.
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
yapf - A formatter for Python files
Behave - BDD, Python style.
autoflake - Removes unused imports and unused variables as reported by pyflakes
nose2 - The successor to nose, based on unittest2
Pylint - It's not just a linter that annoys you!
nose - nose is nicer testing for python
autopep8 - A tool that automatically formats Python code to conform to the PEP 8 style guide.
Schemathesis - Automate your API Testing: catch crashes, validate specs, and save time
pyright - Static Type Checker for Python