How to quickly edit SVG figures in Inkscape (using a custom Xlib-based shortcut manager) and insert the results in a LaTeX document. Gilles Castel is a note-taking wizard.
The author of Trio (Python concurrency library) explains their concept of lexically-structured concurrency
Python's syntax for nested list comprehensions always seemed backwards to me; here's an explanation for why it is the way it is. I still think nested for-loops are easier to read.
On the pitfalls of trying to keep secrets out of Python's memory. [Spoiler: there's no good way.]
I feel like I end up reading this once a year, usually when I'm trying to write a [python] script that needs to run from a cron script without clobbering its own files if one instance is started before the previous one is finished.
See also my shelfcache Python3 package which provides a thread- and multiprocess-safe key-value caching store on top of the standard library's shelve module: https://github.com/cristoper/shelfcache
A python script/package I wrote for converting audio wave files to vector graphics.
Someone wrote this tutorial (Japanese) on using my wav2vec python script [1] to create graphics from audio waveforms.
Google's English translation:
https://translate.google.com/translate?hl=en&sl=ja&tl=en&u=http%3A%2F%2Fwww-b.uec.tmu.ac.jp%2Fshakuhachi%2FSonicPi%2F%23SoundWaveArt
The one where Michael Burge writes a chess engine to demonstrate how to load and execute arbitrary machine code from a Python user-defined function from within Amazon's Redshift database service.
Ben Hoyt's account of contributing a feature to Python's standard library is a good account of the open-source process gone well.
I haven't used Julia for anything yet, but I like that it not only optimizes explicit for-loops, but it also fuses code using vectorized-syntax even for user-defined functions and types. Compared to Matlab/Octave, Python, and R where loops are very (very)* slow and vectorized code is only fast for certain operations and library functions, it is appealing.
Julia also has annotations that can be use to explicitly tell the compiler to turn off bounds checking and emit SIMD instructions for loops.
- The Numba package brings JIT capability to CPython (and so numpy), and probably compares most favorably to Julia than the other languages. Octave also has an experimental JIT compiler (seems like a lot of overhead going from interpreter to machine code), as do recent versions of Matlab (since R2015b). R since 2.14.0 includes the 'compiler' package, a bytecode interpreter and JIT (which compiles to bytecode, not machine code), which probably helps for long-running loops.
Guido recounts some of the history of adding closures to Python.
Brett Cannon traces the evolutionary path from Python 2's generators to Python 3.5's async/await coroutines.
On the futility of Unix file locks (especially on networked file systems).
Note that Linux 3.15 introduced "open file description locks" to fcntl, which combines the semantics of BSD flock (lock associated with open file) with the record-locking and NFS-compatibility of fcntl. That solves everything except for portability.