Episode #247: Do you dare to press "."?
Published Thu, Aug 26, 2021, recorded Wed, Aug 25, 2021.
Watch the live stream:
About the show
Special guest: Dan Taylor
Michael #1: Keep your computer awake during long processing
- For now, use Michael’s fork when on macOS. Until this PR is merged.
- Do you have work that will take a long time?
- Keeping your OS working away is just a context block
from wakepy import keepawake with keepawake(keep_screen_awake=False): ... # do stuff that takes long time
- via Kevin Markham
- The punchline (but it’s not enough)
- Write a brief introduction
- Provide a self-contained code example
- Detail the expected results and why I expect those results
- Add any important notes
- Link to any relevant questions
- Write a title that summarizes the question
- Kevin starts with a question about pandas dataframes and filling in missing values.
- The question is really application specific
- The rewrite of the question is awesome
- Simplifies the problem into a toy example, literally, and out of the domain specific context.
- Includes example code that can copied, pasted, and run that sets up the problem
- Uses short and simple variable names
- Talks about expected results. And why he expects those results.
- Includes a dataset in the sample code that covers cases the solution needs to provide
- Includes non-obvious requirements or non-requirements
- Links to related questions and why they don’t solve your problem.
- I don’t think I’ve ever seen this, but I think it’d be cool to add test code that will pass when the problem is solved. But that might make the question unnecessarily long.
- Fun bonus feature released at the same time as GitHub Codespaces
- Runs VS Code entirely in your browser - supercharged “edit button”
- Nothing to install
- There’s no server to pay for, though functionality is limited
- The file system is your browser’s local storage and GitHub repo
- You can add files and commit changes directly to your repo
- You can install extensions that support running in “VS Code Web”
- Added basic web support to the Python Extension just yesterday
- Syntax checking, auto-complete, go-to-definition
- Uses type hints for packages (no python interpreter in the browser)
- You can also install vscode-pyiodide to run Python code using Jupyter+Pyiodide
- Overall means you can do more powerful code editing quickly in GitHub.com, I’m looking forward to seeing how this evolves
Michael #4: Log analyzer (minus google analytics)
- GoAccess is an open source real-time web log analyzer and interactive viewer that runs in a terminal in *nix systems or through your browser.
- Fast, real-time, millisecond/second updates, written in C
- Only ncurses as a dependency
- Nearly all web log formats (Apache, Nginx, Amazon S3, Elastic Load Balancing, CloudFront, Caddy, etc)
- Simply set the log format and run it against your log
- Beautiful terminal and bootstrap dashboards (Tailor GoAccess to suit your own color taste/schemes)
- recommended by Blaise
- “firmware for computer keyboards written and configured in CircuitPython.”
- Cool list of features
- Fully configured through a single, easy to understand Python file.
- Single-piece or two-piece split keyboards are supported
- Chainable keys such as KC.LWIN(KC.L) to lock the screen on a Windows PC
- Built-in unicode macros, including emojis
- RGB underglow and LED backlights
- One key can turn into many more based on how many times you tap it
- One writeup I found of someone using it for a 10-key
- KMK: run Python on your keyboard
- includes a video
- Seems like limited hardware so far, and although the coding might not be too difficult, you still gotta swap out of the circuitboard.
- I’m bringing this topic up because I’m hoping some keyboard kit people will put together something that just starts with the ability to run CircuitPython so I can just skip to the coding part.
- via Sebastián Ramírez (creator of SQLModel and FastAPI)
- Write a schema once and use everywhere, reduces a lot of repetitive code
- Traditionally have to manage several layers of code to pass your data from database queries, to the backend code, expose to your API and consume from the client
- Code-first ORMs (SQLAlchemy, Django ORM) make it easy to write code that generates SQL
- FastAPI makes it easy to expose objects to your API using Pydantic models
- Before you would need to create both models and convert from ORM to Pydantic using .from_orm
- SQLModel unifies those: a SQLModel is both a SQLAlchemy model and a Pydantic model
- You can use SQLModel to interact with the database (via wrapping SQLAlchemy)
- You can use that same model as a Pydantic model in FastAPI requests and responses
- FastAPI also uses the Pydantic models to generate an openapi.json, meaning you could generate a client library in any language using OpenAPI Generator
- Some other cool things:
- Designed using type annotations so that editors like VS Code, PyCharm give great auto-complete out of the box, uses the proposed dataclass_transforms spec for dynamic typing
- Supports async database sessions, alembic migrations because it’s based on SQLAlchemy (not yet documented)
- Should be possible to integrate with postgis, ts_vectors
- pip install ./local_directory is pretty interesting. Test & Code 163
- The way pip installs from a local directory is about to change. Stéphane Bidoul joins the show to talk about it.
- type4py - using ML to add type annotations to your codebase
- retrofitting codebases with types is a pain — static type checkers can only infer so much
- type4py research paper outlines a state of the art ML model for inferring types, adopting some techniques used in computer vision
- Open sourced training code, data set, VS Code extension, and inferencing server
- If you have a need to add type annotations to a large code base, worth giving this a try!
Joke: Continuous Deployment