hypothesis
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hypothesis | cookiecutter | |
---|---|---|
20 | 56 | |
7,275 | 21,572 | |
1.5% | 1.4% | |
9.9 | 8.6 | |
about 23 hours ago | 2 days ago | |
Python | Python | |
GNU General Public License v3.0 or later | 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.
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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
cookiecutter
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Ask HN: How do you bootstrap your software projects?
Sometimes I use this to abstract boilerplate https://github.com/cookiecutter/cookiecutter
It can use a repo as a template.
It supports some interactive questions to choose options but mostly it is jinja templates.
Having libraries would be another option.
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FastStream: Python's framework for Efficient Message Queue Handling
Install the cookiecutter package using the following command:
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Template for Django Projects
Consider taking a look at cookiecutter to generate projects from templates. There is also cookiecutter-django. As for your environment variables you should have an example .env file containing all the environment variables required by your project (without setting them) that can be safely pushed into your repository for you and other developers to copy into the actual .env file that'll be used by your project (add this file to .gitignore)
- Rmarkdown/Github project organization question
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Python Cookiecutter: Streamline Template Projects for Enhanced Developer Experience
The Python Cookiecutter library revolutionizes project development by offering streamlined approach to creating template projects and improving developer experience.
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What do you use to generate Terraform/Grunt files at scale?
We use cookie cutter templates (the Python project, https://github.com/cookiecutter/cookiecutter ), we prompt for the module & version etc
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A Python package that has a basic app setup inside it
Why not use cookiecutter or a similar tool designed for making these sorts of project templates?
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Sub library with useful code
Is it common? I don't know. Is it useful? Absolutely. There is a tool called cookiecutter that allows you to define your own setup. For example, my cookiecutter setup for a python library is here. You can see what it's like by first installing the cookiecutter cli and then running
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New tool: Souce code generator from a given template
Also cookiecutter.
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Introducing Visual Cookiecutter: a web UI for instanciating cookiecutter templates
Visual Cookiecutter enhances the functionality of cookiecutter by offering unique features such as required fields, conditional input parameters, optional descriptions, and the ability to fix mistakes easily. This package seamlessly integrates with cookiecutter so that all existing templates work out-of-the-box.
What are some alternatives?
pytest - The pytest framework makes it easy to write small tests, yet scales to support complex functional testing
copier - Library and command-line utility for rendering projects templates.
Robot Framework - Generic automation framework for acceptance testing and RPA
Jinja2 - A very fast and expressive template engine.
Behave - BDD, Python style.
backstage - Backstage is an open platform for building developer portals
nose2 - The successor to nose, based on unittest2
try - Dead simple CLI tool to try Python packages - It's never been easier! :package:
nose - nose is nicer testing for python
bashplotlib - plotting in the terminal
Schemathesis - Automate your API Testing: catch crashes, validate specs, and save time
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