thomasaarholt

import numpy as np
import matplotlib
import matplotlib.pyplot as plt
Arc = matplotlib.patches.Arc

def halfangle(a, b):
    "Gets the middle angle between a and b, when increasing from a to b"
    if b < a:
        b += 360
    return (a + b)/2 % 360

Throwaway-MM

// This is a hack, a quick and dirty console script for RT/tweets (with replies) removal w/o API
// To be used in: https://twitter.com/Username/with_replies
// Set your username (without @) below (case-sensitive) to correctly trigger the right Menu

const tweetUser = 'Username'

// BUG, With above we still trigger Menu on some replies but relatively harmless.

// @Hack Implement simple has() for querySelector
const querySelectorHas = function( parent, child ){

aymericbeaumet

Ever wanted to delete all your likes/favorites from Twitter but only found broken/expensive tools? You are in the right place.

1. Go to: https://twitter.com/{username}/likes
2. Open the console and run the following JavaScript code:

setInterval(() => {
  for (const d of document.querySelectorAll('div[data-testid="unlike"]')) {
    d.click()
 }

bluetidepro

// I did this in Chrome...
// 
// Go to https://www.facebook.com/ads/preferences/
// Click the "Advertisers" section to open it up.
// Click "See more" once
// Before doing anything else, just keep clicking space bar to trigger the "see more" button
// Do this for a bit until all the advertisers are loaded
// then run this below in the dev tools console...
// (It will take a few minutes, depending how many you have, and your browser may lock up, but once it's done you will see it auto clicked the "remove" X in the top right for all of them)

mosquito

Docker compose as a systemd unit

Create file /etc/systemd/system/docker-compose@.service. SystemD calling binaries using an absolute path. In my case is prefixed by /usr/local/bin, you should use paths specific for your environment.

[Unit]
Description=%i service with docker compose
PartOf=docker.service
After=docker.service

GaelVaroquaux

Cython example of exposing C-computed arrays in Python without data copies

The goal of this example is to show how an existing C codebase for numerical computing (here c_code.c) can be wrapped in Cython to be exposed in Python.

The meat of the example is that the data is allocated in C, but exposed in Python without a copy using the PyArray_SimpleNewFromData numpy