I don't think Julia's error message situation is particularly worse than C
(where memory access bugs trigger crashes randomly) or Mathematica (where
many bugs end in infinite loops of symbolic computations that eat up all
available memory). I also think teaching in Python would be harder, since
it requires OOP concepts. Matlab teaches too many bad programming habits,
hides what's going on from the students, and is not a particularly
employable skill (as we've been told by prospective employers).
So while Julia has its quirks, I don't see another language that is clearly
better.
On Saturday, April 16, 2016 at 12:01:05 PM UTC+10, Peter Kovesi wrote:
>
>
> Sheehan, That's a very nice looking course but I think you are very brave
> to use Julia at this stage.
> I love the language but (at this stage of the language's development) the
> error reporting is highly problematic. For example this morning I made a
> classic mistake
>
> function foo(a::real) # Should have been: function foo(a::Real)
> ...
> end
>
> The function was defined at line 998, the error was reported at line 433,
> 565 lines away! The message was
> "ERROR: LoadError: TypeError: Tuple: in parameter, expected Type{T}, got
> Function"
>
> Good luck to your students!
>
> Working in Julia requires a practice of defensive incremental coding in
> the extreme. Every few lines of code that are added need to be tested
> before carrying on. That way you know that any errors are in the few lines
> of code that were just added and not at whatever spurious location was
> being suggested.
>
> Let me say again I love the language. However the error reporting is a
> source of extreme frustration to me.
>
> A key pathway to getting Julia more widely adopted would be for it to be
> used for teaching purposes. However, at the moment I fear that any attempt
> to do so would surely end in tears.
>
> Peter Kovesi
>
>
> On Friday, April 15, 2016 at 10:17:40 AM UTC+8, Sheehan Olver wrote:
>>
>>
>>
>> I'm currently lecturing the course MATH3076/3976 Mathematical Computing
>> at U. Sydney in Julia, and thought that others may be interested in the
>> resources I've provided:
>>
>> http://www.maths.usyd.edu.au/u/olver/teaching/MATH3976/
>>
>> The lecture notes and labs are all Jupyter notebooks. I've also included
>> a "cheat sheet" of Julia commands used in the course
>>
>>
>> http://nbviewer.jupyter.org/url/www.maths.usyd.edu.au/u/olver/teaching/MATH3976/cheatsheet.ipynb
>>
>> The course is ongoing (it's about half through) and will continue to take
>> shape, but any feedback is of course welcome!
>>
>>
>> Sheehan Olver
>>
>
