tomchristie

import trio

async def main():
    async with ws_connect("ws://127.0.0.1:8765") as websockets:
        await websockets.send("Hello, world.")
        message = await websockets.recv()
        print(message)

trio.run(main)

samuelcolvin

Python People

(Updated 2022-11-16 with suggestions from comments below, Twitter and Mastodon)

An incomplete list of people in the Python community to follow on Twitter and Mastodon.

With the risk that Twitter dies, I'd be sad to lose links to interesting people in the community, hence this list.

I would love you to comment below with links to people I've missed.

samuelcolvin

"""
License: MIT
Copyright (c) 2022 Samuel Colvin.

See https://twitter.com/adriangb01/status/1573708407479189505

## Usage

Once installed just add

nymous

Logging setup for FastAPI

This logging setup configures Structlog to output pretty logs in development, and JSON log lines in production.

Then, you can use Structlog loggers or standard logging loggers, and they both will be processed by the Structlog pipeline (see the hello() endpoint for reference). That way any log generated by your dependencies will also be processed and enriched, even if they know nothing about Structlog!

Requests are assigned a correlation ID with the asgi-correlation-id middleware (either captured from incoming request or generated on the fly). All logs are linked to the correlation ID, and to the Datadog trace/span if instrumented. This data "global to the request" is stored in context vars, and automatically added to all logs produced during the request thanks to Structlog. You can add to these "global local variables" at any point in an endpoint with `structlog.contextvars.bind_contextvars(custom

adriangb

from __future__ import annotations

import cProfile
import pstats
from collections import deque
from dataclasses import dataclass, field
from random import Random
from timeit import default_timer
from typing import Awaitable, Callable, Deque, Iterable, List, Optional, Protocol, Union

codemation

# Cron Pattern
import asyncio
from fastapi import FastAPI

server = FastAPI()

server.cron_jobs = asyncio.Queue()

def cron(interval: int):
    def cron_setup(f):

zzzeek

"""This program is exactly the same as that of

https://gist.github.com/zzzeek/33943060f7a08cf9e82bf8df1f0f75de ,
with the exception that the add_and_select_data function is written in
synchronous style.

UPDATED!!   now includes refinements by @snaury and @Caselit .  SIMPLER
AND FASTER!!

dmontagu

# Based on the example from https://www.activestate.com/blog/dash-vs-bokeh/
import dash
import dash_core_components as dcc
import dash_html_components as html
import pandas as pd
import plotly.graph_objs as obj
import uvicorn as uvicorn
from dash.dependencies import Input, Output
from fastapi import FastAPI
from starlette.middleware.wsgi import WSGIMiddleware

shimmerjs

apiVersion: skaffold/v1alpha2
kind: Config
# default build profile builds every image in the repository without pushing
# used to verify build
build:
  tagPolicy:
    sha256: {}
  artifacts:
  - imageName: &grady registry.ng.bluemix.net/dev-infra/grady
    # &ws -> "workspace"

dhagrow

"""
Python is a dynamic language, and it is relatively easy to dynamically create
and modify things such as classes and objects. Functions, however, are quite
challenging to create dynamically.

One area where we might want to do this is in an RPC library, where a function
defined on a server needs to be available remotely on a client.

The naive solution is to simply pass arguments to a generic function that
accepts `*args` and `**kwargs`. A lot of information is lost with this approach,