Mason is a collection of packages that enable developers to create and consume reusable templates called bricks.

#dart #flutter #cli #template #templating

Developing and Testing an Asynchronous API with FastAPI and Pytest.

This tutorial looks at how to develop and test an asynchronous API with FastAPI, Postgres, pytest and Docker using Test-driven Development (TDD). We'll also use the Databases package for interacting with Postgres asynchronously.

#python #article #asyncio #fastapi

Python Concurrency with asyncio.

Learn how to speed up slow Python code with concurrent programming and the cutting-edge asyncio library.

- Use coroutines and tasks alongside async/await syntax to run code concurrently
- Build web APIs and make concurrency web requests with aiohttp
- Run thousands of SQL queries concurrently
- Create a map-reduce job that can process gigabytes of data concurrently
- Use threading with asyncio to mix blocking code with asyncio code

💀Achtung! Be aware that piracy is punishable by law in most countries.

I strongly recommend that you buy books (as I do) and do not encourage anyone to pirate.

🏴‍☠️For pirates (use Tor): pdf

#book #recommendation #asyncio

Trio – a friendly Python library for async concurrency and I/O.

The Trio project aims to produce a production-quality, permissively licensed, async/await-native I/O library for Python. Like all async libraries, its main purpose is to help you write programs that do multiple things at the same time with parallelized I/O. A web spider that wants to fetch lots of pages in parallel, a web server that needs to juggle lots of downloads and websocket connections simultaneously, a process supervisor monitoring multiple subprocesses... that sort of thing. Compared to other libraries, Trio attempts to distinguish itself with an obsessive focus on usability and correctness. Concurrency is complicated; we try to make it easy to get things right.

#python #async #event #loop #networking #io #trio #concurrency

Motor - the async Python driver for MongoDB and Tornado or asyncio.

#python #asyncio #mongodb #db #orm #driver #tornado

aiopg is a library for accessing a PostgreSQL database from the asyncio framework. It wraps asynchronous features of the Psycopg database driver.

#python #asyncio #postgres #driver #pg #sqlalchemy

Ivy is an ML framework that currently supports JAX, TensorFlow, PyTorch, MXNet, and Numpy.

#python #ml #dl #pytorch #neural #network #mxnet #gpu #numpy

Term.jl is a Julia library for stylized terminal output.

Term.jl uses a simple markup syntax to add style information to standard Julia strings. It also provides Renderable objects such as the Panel and TextBox as you can see in the example below. These too can be styled, include styled text, and they can be nested and stacked to produce structured visual displays in your terminal.

#julia #colored #output #terminal

Tapir is a declarative, type-safe web endpoints library.

With tapir, you can describe HTTP API endpoints as immutable Scala values. Each endpoint can contain a number of input and output parameters.

#scala #jvm #endpoints #http #api #akka

Genie is a full-stack web framework that provides a streamlined and efficient workflow for developing modern web applications. It builds on Julia's strengths (high-level, high-performance, dynamic, JIT compiled), exposing a rich API and a powerful toolset for productive web development.

#julia #web #framework #reactive #orm #mvc

Nextflow is a bioinformatics workflow manager that enables the development of portable and reproducible workflows. It supports deploying workflows on a variety of execution platforms including local, HPC schedulers, AWS Batch, Google Cloud Life Sciences, and Kubernetes. Additionally, it provides support for manage your workflow dependencies through built-in support for Conda, Docker, Singularity, and Modules.

#groovy #jvm #docker #aws #cloud #bioinformatics #hpc #dataflow

dplyr is a grammar of data manipulation, providing a consistent set of verbs that help you solve the most common data manipulation challenges:

- mutate() adds new variables that are functions of existing variables
- select() picks variables based on their names.
- filter() picks cases based on their values.
- summarise() reduces multiple values down to a single summary.
- arrange() changes the ordering of the rows.

#r #R #grammar #data #manipulation