Julia for Python programmers

One of my clients is writing software in Julia so I’m picking up the language. I looked at Julia briefly when it first came out but haven’t used it for work. My memory of the language was that it was almost a dialect of Python. Now that I’m looking at it a little closer, I can see more differences, though the most basic language syntax is more like Python than any other language I’m familiar with.

Here are a few scattered notes on Julia, especially on how it differs from Python.

  • Array indices in Julia start from 1, like Fortran and R, and unlike any recent language that I know of.
  • Like Python and many other scripting languages, Julia uses # for one-line comments. It also adds #= and =# for multi-line comments, like /* and */ in C.
  • By convention, names of functions that modify their first argument end in !. This is not enforced.
  • Blocks are indented as in Python, but there is no colon at the end of the first line, and there must be an end statement to close the block.
  • Julia uses elseif as in Perl, not elif as in Python [1].
  • Julia uses square brackets to declare a dictionary. Keys and values are separated with =>, as in Perl, rather than with colons, as in Python.
  • Julia, like Python 3, returns 2.5 when given 5/2. Julia has a // division operator, but it returns a rational number rather than an integer.
  • The number 3 + 4i would be written 3 + 4im in Julia and 3 + 4j in Python.
  • Strings are contained in double quotes and characters in single quotes, as in C. Python does not distinguish between characters and strings, and uses single and double quotes interchangeably.
  • Julia uses function to define a function, similar to JavaScript and R, where Python uses def.
  • You can access the last element of an array with end, not with -1 as in Perl and Python.

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[1] Actually, Perl uses elsif, as pointed out in the comments below. I can’t remember when to use else if, elseif, elsif, and elif.

Julia random number generation

Julia is a new programming language for scientific computing. From the Julia site:

Julia is a high-level, high-performance dynamic programming language for technical computing, with syntax that is familiar to users of other technical computing environments. It provides a sophisticated compiler, distributed parallel execution, numerical accuracy, and an extensive mathematical function library. …

I just started playing around with it. I didn’t see functions for non-uniform random number generation so I wrote some as a way to get started.

[Update: there are non-uniform random number generators in Julia, but they have not been added to the documentation yet. See details in this comment.]

Here’s a random number generator for normal (Gaussian) random values:

## return a random sample from a normal (Gaussian) distribution
function rand_normal(mean, stdev)
    if stdev <= 0.0
        error("standard deviation must be positive")
    u1 = rand()
    u2 = rand()
    r = sqrt( -2.0*log(u1) )
    theta = 2.0*pi*u2
    mean + stdev*r*sin(theta)

From this you can see Julia is a low-ceremony language: Python-like syntax, you can call common mathematical functions without having to do anything special, etc. You can have explicit return statements, but the preferred style seems to be to let the last line of the function be the implicit return statement.

My most common mistake so far has been forgetting to close code blocks with end; Julia’s syntax is similar enough to Python that I suppose I think indentation should be sufficient.

I’ve written random number generators for the following probability distributions:

  • Beta
  • Cauchy
  • Chi square
  • Exponential
  • Inverse gamma
  • Laplace (double exponential)
  • Normal
  • Student t
  • Uniform
  • Weibull

You can find the code here: Non-uniform random number generation in Julia.