chai VS pyenv

Let's configure tests with mocha, chai and sinon on Typescript in a few steps.

These options totally depend on your requirements. For example an empty hardhat option is a very basic project sample if you need more libraries, plugins or you use Typescript later, you have to add them manually. Like @nomiclabs/hardhat-waffle , [ethereum-waffle](https://www.npmjs.com/package/ethereum-waffle), chai , @nomiclabs/hardhat-ethers , ethers , [@openzeppelin/contracts](https://www.npmjs.com/package/@openzeppelin/contracts) Because, they'll allow us to interact with Ethereum and to test our contracts.

For those of you wondering what a "Chaijs" is like I was: https://github.com/chaijs/chai

> BDD / TDD assertion framework for node.js and the browser that can be paired with any testing framework.

ES6 test setup with Mocha and Chai.

It'd be hard for me to imagine what would it be like back then to write tests if it wasn't for Enzyme. Even with Enzyme, it was noticeably harder than it is today, and I still vividly remember installing and configuring in my project Mocha, Chai, Sinon, and JSDOM, to get pretty much the same tooling that today a single library - Jest - provides out of the box, while adding numerous features on top of that.

The library offers a BDD testing style and fully exploits javascript promises - the resulting tests are simple, clear and expressive. Chakram is built on node.js, mocha, chai and request.

For this example, I am using Mocha, plus Chai for its BDD expect syntax and the chai-as-promised plugin for asynchronous matchers.

chai

If you have a requirement for multiple, specific Python versions, why not just use pyenv?

https://github.com/pyenv/pyenv

For more information visit: pyenv repository

Note that virtual environments assume you are using the same global version of Python. Often, this is not the case and additional tools like pyenv can be used alongside virtual environments when you need to switch between versions of Python itself on your local machine.

Python version manager pyenv

First of all, install the Python virtual environment from these links: 1 and 2. I developed my GPT-based API in Python version 3.8.18. Pick any Python versions >= 3.7.

Note: Most modern systems will probably have a system environment that meets this requirement, but if yours does not or if you prefer not to install anything in your system environment (even if it's just PDM) check out asdf or pyenv to help install and manage additional Python environments.

When dealing with software development, reproducibility is key. This is why we encourage you to use Python virtual environments to set up an isolated environment for your project. Virtual environments allow the isolation of dependencies, which plays a crucial role to avoid breaking compatibility between different projects. We cannot cover all the details about virtual environments in this post, but we encourage you to learn more about venv, pyenv or conda for a better understanding on how to create and manage virtual environments.

For Python, with your use case I would avoid system packages, no matter the distro. It sounds like it would be worth setting up pyenv and working exclusively with virtual environments.

For OSX there is homebrew or pyenv (pyenv is another solution on Linux). As pyenv compiles from source it will require setting up XCode (the Apple IDE) tools to support this which can be pretty bulky. Windows users have chocolatey but the issue there is it works off the binaries. That means it won't have the latest security release available since those are source only. Conda is also another solution which can be picked up by Visual Studio Code as available versions of Python making development easier. In the end it might be best to consider using WSL on Windows for installing a Linux version and using that instead.