This behaviour is already bullet 2 and 3 on the list you suggested. Emphasizing that Matlab implicitly inserts a copy(x), when you mutate arrays after some boundaries might be a good idea. I also find it somewhat hard to grasp the consequences of the current wording, but I'm not the target audience for that part of the documentation.
As always when you want to change something in Julia, the prefered way is to submit a pull request on Github. For documentation, you can even use the github web ui <https://github.com/JuliaLang/julia/edit/master/doc/manual/noteworthy-differences.rst> to edit. If you open a pull request you should also link to this thread as previous discussion. Regards Ivar kl. 16:35:52 UTC+2 mandag 13. oktober 2014 skrev Stephan Buchert følgende: > > Thanks for the explanations. > > To me this seems now reasonable, after realizing that in Julia not only > reshape etc, but already the normal assignment b=a (for a an array) opens > for the same kind of "surprises" as I had noticed them with reshape. Coming > from Matlab this is a surprise, but for someone being familiar with certain > other languages, I guess, it is not? > > julia> a=[1, 2, 3] > 3-element Array{Int64,1}: > 1 > 2 > 3 > > julia> b=a; > > julia> b[2]=0 > 0 > > julia> a > 3-element Array{Int64,1}: > 1 > 0 > 3 > > But then "+-" is different again: > > julia> b+=1; > > julia> a > 3-element Array{Int64,1}: > 1 > 0 > 3 > > > Perhaps one could add in > http://julia.readthedocs.org/en/latest/manual/noteworthy-differences/ > under Matlab near the top something like: > > * Arrays are mutable. Assignments like b=a and functions b=f(a) behave in > Matlab always equivalent to b=copy(a) and b=f(copy(a)), but in Julia b > becomes conceptually a pointer or different view of the contents of a. The > values of a can be altered through b. > > PS: Yes, linear indices work directly also in Julia, I had overlooked this > > Am Montag, 13. Oktober 2014 10:33:26 UTC+2 schrieb Jutho: >> >> Some more comments / answers: >> >> I too was confused by the precise use of ! when I first started using >> Julia in cases like reshape. However, it is very clear that reshape should >> not have an exclamation mark. >> >> The use of ! in Julia functions is whenever the function modifies one of >> its input arguments. This can be for several reasons: >> >> 1. The function performs some operation in-place: e.g. >> scale!(A,lambda) multiplies all elements of the array A with the scalar >> lambda in place. >> 2. The operation stores the result of the computation in one >> (typically the first) argument: e.g. copy!(B,A), permutedims!(B,A,perm), >> scale!(B,A,lambda), Base.transpose!(B,A): do something with the data of A >> and store the result in the pre-allocated but not necessarily initialised >> array B >> 3. The function overwrites the input arguments for use as temporary >> workspace, but the actual result is just a normal return value as in >> typical functions: e.g. the different [eig,svd,qr]fact!(A) methods >> >> The Base.LinAlg.BLAS methods do something in between 1. and 2.; they >> store the result of the computation in a dedicated 'output' argument as in >> 2., but they can also just add the result to that output argument, in which >> case it needs to be correctly initialised. >> >> In summary, the exclamation mark means that if you need the original >> values of all of the arguments after calling he function, you should better >> make a copy, since calling the function allows it to destroy the original >> values of its arguments (only possible with arrays or other mutable types). >> Also note that most of these functions do not change the size of array >> arguments, resize! is one of the notable few exceptions and only works with >> one-dimensional arrays (vectors). >> >> reshape doesn't fit in any of these categories. B=reshape(A,newsize) does >> not destroy the values of a. It is however true that the output B shares >> data with A, just like if you do B=slice(A,...) or B=sub(A,...). Note that >> none of these functions use an exclamation mark, nor is there any other >> stylistic convention to indicate that this happens. That's just a new >> aspect of the language that you have to take into account when coming from >> e.g. Matlab, but that is very natural when coming from e.g. C. >> >> Finally, note that linear indexing indeed works on an array without >> reshaping, but that if you want the 'vectorized' version a multidimensional >> array, there is the build in function B=vec(A), which is indeed equivalent >> to B=reshape(A,prod(size(A))) >> >> >> >> >> Op zaterdag 11 oktober 2014 22:20:13 UTC+2 schreef Stephan Buchert: >>> >>> julia> a=[1 2; 3 4; 5 6] >>> 3x2 Array{Int64,2}: >>> 1 2 >>> 3 4 >>> 5 6 >>> >>> julia> b=reshape(a, prod(size(a))); >>> >>> julia> b[3]=0 >>> 0 >>> >>> julia> a >>> 3x2 Array{Int64,2}: >>> 1 2 >>> 3 4 >>> 0 6 >>> >>>
