align_images.py

import argparse
import cv2
import dlib
import json
import numpy
import skimage
from pathlib import Path
from tqdm import tqdm
from umeyama import umeyama

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Comments on optimizations around string concatenation.

Note: The code links are to CPython 3.8.5, the most recent release when this was written.

I was recently asked about a performance optimization in CPython around using += and + for string objects. As some people may already know, if you use += or + a string, it can sometimes be just as fast as ''.join. The question was to explain when that optimization couldn't be performed.

We will be going through the following example scenarios:

georgexsh

import sys

def j(lineno):
    frame = sys._getframe().f_back
    called_from = frame

    def hook(frame, event, arg):
        if event == 'line' and frame == called_from:
            try:
                frame.f_lineno = lineno

simonw

How to recover lost Python source code if it's still resident in-memory

I screwed up using git ("git checkout --" on the wrong file) and managed to delete the code I had just written... but it was still running in a process in a docker container. Here's how I got it back, using https://pypi.python.org/pypi/pyrasite/ and https://pypi.python.org/pypi/uncompyle6

Attach a shell to the docker container

Install GDB (needed by pyrasite)

apt-get update && apt-get install gdb

dbehnke

"""
client.py - AsyncIO Server using StreamReader and StreamWriter

This will create 200 client connections to a server running server.py

It will handshake and run similar to this:

Server: HELLO
Client: WORLD

nadavrot

High-Performance Matrix Multiplication

This is a short post that explains how to write a high-performance matrix multiplication program on modern processors. In this tutorial I will use a single core of the Skylake-client CPU with AVX2, but the principles in this post also apply to other processors with different instruction sets (such as AVX512).

Intro

Matrix multiplication is a mathematical operation that defines the product of