Interesting point of view. Choices of style apart, there are situations where textures are useful. Like for example the case faced by swayvil (see the comment), or when you need b/w printable images.
Another good reference is "Semiology of Graphics" of Jacques Bertin, he has a good opinion about textures.
Pykov implements the computation of some of the most common quantities related to discrete-time finite regular Markov Chains, namely: steady state, mean first passage times and absorbing times. The calculation are performed by means of the analytic formulas described on Kemeny&Snell, and the steady state is derived with the inverse iteration method. If you are interested on random walkers, pykov offers a handy way to generate them and evaluate their probability.
Sorry, I do not know PyMC well enough to say if the above quantities can be calculated also with it.
I coded this library during my PhD, where I applied Markov Chains to the study of protein folding dynamics. I am quite confident with it, but as far as I know few others used it. At that time, I used pykov with networks of around 100k nodes and 500k links. Only recently I replaced pysparse with scipy sparse solvers. Entire methods are comment out because they are heuristics that do not belong to the standard Markov Theory, and they can be removed.
Agree. Naming is difficult and here maybe I did a wrong choice. Anyway, pykov.Vector class inherit from dict, which is not ordered, so mutating in place cannot be done. The sort() method returns a list, which makes the name "sort" even more problematic. What do you suggest? (OrderedDict?)