Tomorrow we begin with the joys of functions and sorting.  I remembered how slow the basic recursive fibonacci calculation could be and decide to compare Python to Julia.  Both are running on one of four CPUs and garbage collection is on so no cheating.  The code is:

	def fib(n):
	""" old fib exponential complexity ? """
	if n == 0 or n == 1:
	return 1
	else:
	return fib(n - 1) + fib(n - 2)
	#Julia
	fib(n::Integer) = return(n > 1 ? (fib(n-1) + fib(n-2)) : one(n))
	# ::Integer defines a function for integers only
	# Julia's multiple dispatch lets you define methods for different
	# types matching against all unnamed types in the method definition
	# As suggested by Steven Johnson, using one(n) rather than 1
	# ensures that the result has the same type as n

view raw gistfile1.py hosted with ❤ by GitHub

Well, for fib(50) Julia took 107 seconds.  Seems slow until you find that Python took (actually I'm still waiting and it started before I wrote the Julia code) ... ... 

I also started learning a bit about Julia's Glamor of Graphics style plotting module, Gadfly.  Here's todays experiment:

Still waiting for Python to finish ... perhaps after dinner ... 1 hour 10 minutes and 5 seconds.  So Julia is 39 times faster than Python calculating the fib.