Like many of you, I watched in horror two days ago as the night unfolded, and the unthinkable slowly came to pass. After a Netflix binge to try to numb the fear, I dived into a clickhole of social media posts and news articles to try to make sense of what had happened. I hope that writing a synthesis of that will let me get on with my life in this brave new world.
I am deeply, depressingly pessimistic about the future of the planet under Trump. Let's take the very best, most ludicrously optimistic scenario: that Trump swings to the center1 and doesn't make good on his many horrid promises. Even then, his election, and the Republicans' victory in the House and Senate, represent game over in the fight to avoid climate change2.
Review: Sony Digital Paper
Three years ago I excitedly posted about Sony's then-new writeable e-paper tablet, called Sony Digital Paper System (DPS-1).
Now it is finally mine and I love it.
Here's what I wrote about it when Sony announced it:
the iPad (et al) sucks for some things. Three of those are: (1) taking handwritten notes, (2) reading (some) pdfs in full-page view, and (3) reading in full daylight. By the sound of it, Sony’s new tablet will excel at all three
Having had it for about a month, I can confidently say that it does indeed excel at those three things, beyond my wildest dreams. Even with the improved competition of the identically-priced iPad Pro (which can now handle points 1 and 2 with aplomb), I still prefer the Sony. Here's why:
Why scientists should code in the open
All too often, I encounter published papers in which the code is "available upon request", or "available in the supplementary materials" (as a zip file). This is not just poor form. It also hurts your software's future. (And, in my opinion, when results depend on software, it is inexcusable.)
Given the numerous options for posting code online, there's just no excuse to give code in a less-than-convenient format, upon publication. When you publish, put your code on Github or Bitbucket.
In this piece, I'll go even further: put your code there from the beginning. Put your code there as soon as you finish reading this article. Here's why:
The cost of a Python function call
I've read in various places that the Python function call overhead is very high. As I was parroting this "fact" to Ed Schofield recently, he asked me what the cost of a function actually was. I had no idea. This prompted us to do a few quick benchmarks.
The short version is that it takes about 150ns to call a function in Python (on my laptop). This doesn't sound like a lot, but it means that you can make at most 6.7 million calls per second, two to three orders of magnitude slower than your processor's clock speed.
If you want your function to do something, such as, oh, I don't know, receive an input argument, this goes up to 350ns, throttling you at 2.8 million calls per second.
My first use of Python 3's `yield from`!
I never really understood why
yield from was useful. Last weekend, I wanted to use Python 3.5's new
os.scandir to explore a directory (and its subdirectories). Tragically,
os.scandir is not recursive, and I find
os.walks 3-tuple values obnoxious.
Lo and behold, while I was trying to implement a recursive version of
yield from use just popped right out!
import os def rscandir(path): for entry in os.scandir(path): yield entry if entry.is_dir(): yield from rscandir(entry.path)
That's it! I have to admit that reads wonderfully. The Legacy Python (aka Python 2.x) alternative is quite a bit uglier:
import os def rscandir(path): for p in os.listdir(path): yield p if os.path.isdir(p): for q in rscandir(p): yield q
Yuck. So, yet again: time to move away from Legacy Python! ;)
EuroSciPy 2015 debrief
The videos from EuroSciPy 2015 are up! This marks a good time to write up my thoughts on the conference. I’ve mentioned before that the yearly SciPy conference is stunningly useful. This year I couldn’t make it to Austin, but I did attend EuroSciPy, the European version of the same conference, in Cambridge, UK. It was spectacular.