Python Weekly (Issue 446 April 23 2020)

Python Weekly - Issue 446

April 23, 2020

Python Weekly

Welcome to issue 446 of Python Weekly. Let's get straight to the links this week.

From Our Sponsor 

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Articles, Tutorials and Talks

Imagine throwing a ball and if it hits the backboard it somehow gets directed into the hoop. Thanks to physics it isn't quite possible to make ALL possible shots go in though you can make a hoop where the ball goes in a lot more often. This video shows you how to write a program to calculate the optical backboard then machined and fabricated it.

About a year ago, I published a Rust crate called inline-python, which allows you to easily mix some Python into your Rust code using a python!{ .. } macro. In this series, I’ll go through the process of developing this crate from scratch.

  • Part 1A - Before continuing to extend our python!{} macro in part 2, let’s explore some things in more detail first

Discovering the quirks of Python's context manager.

Learn Data Analysis with Python in this comprehensive tutorial for beginners, with exercises included! In this tutorial you'll learn the whole process of Data Analysis: reading data from multiple sources (CSVs, SQL, Excel, etc), processing them using NumPy and Pandas, visualize them using Matplotlib and Seaborn and clean and process it to create reports. Additionally, we've included a thorough Jupyter Notebook tutorial, and a quick Python reference to refresh your programming skills.

The most intuitive way of building and implementing Finite State Machines is by using Python Coroutines and in this article, we find how and why.

Let's walk through how one would build their own end-to-end speech recognition model in PyTorch. The model we'll build is inspired by Deep Speech 2 (Baidu's second revision of their now-famous model) with some personal improvements to the architecture. The output of the model will be a probability matrix of characters, and we'll use that probability matrix to decode the most likely characters spoken from the audio. 

In this video we will manipulate the DOM, use local storage, mke Ajax calls, etc using Python in the browser with Brython.

A walkthrough of descriptor protocol to understand inner working of property, classmethod, staticmethod builtins.

The article describes not just a tiny library that does just one essential thing, but some of the backstory of how it went from being a humble utility in a project somewhere to being a slightly less humble, productionized library.

In this tutorial, you will learn how to create a dynamic map using time series data of Coronavirus infections around the world using GeoPandas and Pandas Python libraries. The time series data are acquired from the 2019 Novel Coronavirus COVID-19 (2019-nCoV) Data Repository by Johns Hopkins CSSE.

How to setup Django with Webpack, without plugins to glue them together, using both Django static and Webpack.

Ensemble learning is a machine learning paradigm where multiple models (often called “weak learners”) are trained to solve the same problem and combined to get better results. There are three most common types of ensembles: + Bagging + Boosting + Stacking. In this post we will start with bagging, and then move on to boosting and stacking in separate publications.

This video will be an introduction to using docker-compose to connect a nginx container to a flask application container.

A scraping tutorial with a real world use-case.

Interesting Projects, Tools and Libraries

MONAI is a freely available, community-supported, PyTorch-based framework for deep learning in healthcare imaging. It provides domain-optimized foundational capabilities for developing healthcare imaging training workflows in a native PyTorch paradigm.

Game Boy emulator written in Python.

A simple browser-based hexapod robot simulator built from first principles.

Fork of Python 2.7 with new syntax, builtins, and libraries backported from Python 3.

A website that provides useful documentation on Django’s 57 built-in template filters and 27 built-in template tags. While the official documentation is very good, we have attempted to organize the tags and filters in a more meaningful way, and to give a better sense of how and if/when to use each one.

High Quality Monocular Depth Estimation via Transfer Learning.

A python script that organizes a folder like Downloads based on file extensions.

Literally a Chaos Monkey for your Kubernetes clusters.

clikan is a super simple personal kanban board that runs in a CLI.

QuickSQL is a simple MSSQL query tool that allows you to connect to MSSQL databases and does not require administrative level rights to use.

A Nintendo Switch checkout bot. Currently supports Walmart and Best buy

Convert text to your own hand writing.

Forth Lisp Python Continuum: A small highly dynamic self-bootstrapping language.

Covid19 Dashboard Web App using Python (Plotly Dash).

TorchServe is a flexible and easy to use tool for serving PyTorch models.

New Releases

The highlights for this release are:

  • Significant speedups when building local directories, by changing behavior to perform in-place builds, instead of copying to temporary directories.

  • Significant speedups in pip list --outdated, by parallelizing network access. This is the first instance of parallel code within pip’s codebase.

  • A new pip cache command, which makes it possible to introspect and manage pip’s cache directory.

  • Better pip freeze for packages installed from direct URLs, enabled by the implementation of PEP 610.

The release features several major new API additions and improvements, including a significant update to the C++ frontend, Channel Last memory format for computer vision models, and a stable release of the distributed RPC framework used for model-parallel training.

This was a short release where we addressed 43 issues, including ipywidgets support  in Jypyter Notebooks and debugger support for Django and Flask auto-reload. You can check the full list of improvements in our changelog. 

Upcoming Events and Webinars

A hackathon pairing developers and designers with experts in the medical community and others on the front lines fighting The Coronavirus.

SciPy 2020, the 19th annual Scientific Computing with Python conference, will be a virtual conference held July 6-12, 2020. The annual SciPy Conference allows participants from all types of organizations to showcase their latest projects, learn from skilled users and developers, and collaborate on code development.  

The 2nd annual Python Web Conference is a virtual event that features international tech experts presenting on 40 topics such as microservices, identity management, machine learning and CI/CD. The talks are split into 3 exciting tracks. Each track is accessible live via Zoom and participants can chat via Slack channels. Access to presentations and post-event recordings will be available exclusively to registered attendees.

There will be a talk, Changepoints to Improve Forecasts (Estimation Window Selection for A&E Arrivals Forecasts)

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