This is based on a lightning talk I gave at the LA PyLadies October Hackathon. I’m actually not going to go into anything much resembling algorithmic complexity here. What I’d like to do is present a common performance anti-pattern that I see from novice programmers about once every year or so. If I can prevent one person from committing this error, this post will have achieved its goal. I’d also like to show how an intuitive understanding of time required by operations in relation to the size of data they operate on can be helpful....

I find I have to build simulations with increasing frequency in my work and life. Usually this indicates I’m faced with one of the following situations: The need for a quick estimate regarding the quantitative behavior of some situation. The desire to verify the result of a computation or assumption. A situation which is too complex or random to effectively model or understand. Anyone familiar at all with simulation will recognize the last item as the motivating force of the entire field....

Note: This post was updated to work with NetworkX and for clarity. It’s possible this will turn out like the day when Python 2.5 introduced [coroutines][coroutines]. At the time I was very excited. I spent several hours trying to convince my coworkers we should immediately abandon all our existing Java infrastructure and port it to finite state machines implemented using Python coroutines. After a day of hand waving over a proof of concept, we put that idea aside and went about our lives....

I recently stumbled across an implementation of the affine scaling interior point method for solving linear programs that I’d coded up in R once upon a time. I’m posting it here in case anyone else finds it useful. There’s not a whole lot of thought given to efficiency or numerical stability, just a demonstration of the basic algorithm. Still, sometimes that’s exactly what one wants. solve.affine <- function(A, rc, x, tolerance=10^-7, R=0....

Note: This post was edited for clarity. For the final JAPH in this series, I implemented a simple transpiler that converts a small subset of Scheme programs to equivalent Python programs. It starts with a Scheme program that prints 'just another scheme hacker'. (define (output x) (if (null? x) "" (begin (display (car x)) (if (null? (cdr x)) (display "\n") (begin (display " ") (output (cdr x))))))) (output (list "just" "another" "scheme" "hacker")) The program then tokenizes that Scheme source, parses the token stream, and converts that into Python 3....

Note: This post was edited for clarity. This JAPH uses a Turing machine. The machine accepts any string that ends in '\n' and allows side effects. This lets us print the value of the tape as it encounters each character. While the idea of using lambda functions as side effects in a Turing machine is a little bizarre on many levels, we work with what we have. And Python is multi-paradigmatic, so what the heck....

Note: This post was edited for clarity. At this point, tricking python into printing strings via indirect means got a little boring. So I switched to obfuscating fundamental computer science algorithms. Here’s a JAPH that takes in a Huffman coded version of 'just another python hacker', decodes, and prints it. # Build coding tree def build_tree(scheme): if scheme.startswith('*'): left, scheme = build_tree(scheme[1:]) right, scheme = build_tree(scheme) return (left, right), scheme else: return scheme[0], scheme[1:] def decode(tree, encoded): ret = '' node = tree for direction in encoded: if direction == '0': node = node[0] else: node = node[1] if isinstance(node, str): ret += node node = tree return ret tree = build_tree('*****ju*sp*er***yct* h**ka*no')[0] print( decode(tree, bin(10627344201836243859174935587)....

Note: This post was updated to work with Python 3.12. It may not work with different versions. Here’s a JAPH composed solely for effect. For each letter in 'just another python hacker' it loops over each the characters ' abcdefghijklmnopqrstuvwxyz', printing each. Between characters it pauses for 0.05 seconds, backing up and moving on to the next if it hasn’t reached the desired one yet. This achieves a sort of rolling effect by which the final string appears on our screen over time....

Note: This post was updated to work with Python 3.12. It may not work with different versions. No series of JAPHs would be complete without ROT13. This is the example through which aspiring Perl programmers learn to use tr and its synonym y. In Perl the basic ROT13 JAPH starts as: $foo = 'whfg nabgure crey unpxre'; $foo =~ y/a-z/n-za-m/; print $foo; Python has nothing quite so elegant in its default namespace....

Here’s the second in my reformed JAPH series. It takes an anagram of 'just another python hacker' and converts it prior to printing. It sorts the anagram by the indices of another string, in order of their associated characters. This is sort of like a pre-digested Schwartzian transform. x = 'upjohn tehran hectors katy' y = '1D0HG6JFO9P5ICKAM87B24NL3E' print(''.join(x[i] for i in sorted(range(len(x)), key=lambda p: y[p]))) Obfuscation consists mostly of using silly machinations to construct the string we use to sort the anagram....