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Native Julia noteworthy differences from MATLAB

This package tries to minimize the differences between Julia and Matlab. However, for the record, these points are worth considering.

From: https://docs.julialang.org/en/v1/manual/noteworthy-differences/index.html

Although MATLAB users may find Julia's syntax familiar, Julia is not a MATLAB clone. There are major syntactic and functional differences. The following are some noteworthy differences that may trip up Julia users accustomed to MATLAB:

Julia Arrays:

  • Julia arrays are indexed with square brackets, A[i,j].

  • Julia has true one-dimensional arrays. Column vectors are of size N, not Nx1. For example, rand(N) makes a 1-dimensional array.

  • In Julia, to make an one-dimensional arrays,

    • Use ; or , for concatenation. You can think of this as "one-dimensional arrays in Julia are like vertical arrays in MATLAB". So using ; is more intuitive.
    • Don't use space for concatenation, as spaces make two-dimensional arrays.
    [1; 2; 3]
[1, 2, 3]

    give one-dimensional arrays (think of it as vertical array in MATLAB):

    3-element Array{Int64,1}:
 1
 2
 3

    And,

    [1 2 3]

    gives a two-dimensional array (horizontal):

    1×3 Array{Int64,2}:
 1  2  3

  • In Julia, for multi-dimensional arrays

    • Use space between elements for horizontal concatenation.
    • Don't use , for horizontal concatenation!
    • Use ; for vertical concatenation.

  • In Julia, [x,y,z] will always construct a 3-element array containing x, y and z.

    • To concatenate in the first ("vertical") dimension use either vcat(x,y,z) or separate with semicolons ([x; y; z]).
    • To concatenate in the second ("horizontal") dimension use either hcat(x,y,z) or separate with spaces ([x y z]).
    • To construct block matrices (concatenating in the first two dimensions), use either hvcat or combine spaces and semicolons ([a b; c d]).
  • Julia arrays are not copied when assigned to another variable. After A = B, changing elements of B will modify A as well.
  • Julia values are not copied when passed to a function. If a function modifies an array, the changes will be visible in the caller.
  • Julia does not automatically grow arrays in an assignment statement. Whereas in MATLAB a(4) = 3.2 can create the array a = [0 0 0 3.2] and a(5) = 7 can grow it into a = [0 0 0 3.2 7], the corresponding Julia statement a[5] = 7 throws an error if the length of a is less than 5 or if this statement is the first use of the identifier a. Julia has push! and append!, which grow Vectors much more efficiently than MATLAB's a(end+1) = val.
  • The imaginary unit sqrt(-1) is represented in Julia as im, not i or j as in MATLAB.
  • In Julia, literal numbers without a decimal point (such as 42) create integers instead of floating point numbers. As a result, some operations can throw a domain error if they expect a float; for example, julia> a = -1; 2^a throws a domain error, as the result is not an integer (see the FAQ entry on domain errors for details).
  • In Julia, multiple values are returned and assigned as tuples, e.g. (a, b) = (1, 2) or a, b = 1, 2. MATLAB's nargout, which is often used in MATLAB to do optional work based on the number of returned values, does not exist in Julia. Instead, users can use optional and keyword arguments to achieve similar capabilities.
  • In Julia, a:b and a:b:c construct AbstractRange objects. To construct a full vector like in MATLAB, use collect(a:b). Generally, there is no need to call collect though. An AbstractRange object will act like a normal array in most cases but is more efficient because it lazily computes its values. This pattern of creating specialized objects instead of full arrays is used frequently, and is also seen in functions such as range, or with iterators such as enumerate, and zip. The special objects can mostly be used as if they were normal arrays.
  • Functions in Julia return values from their last expression or the return keyword instead of listing the names of variables to return in the function definition (see The return Keyword for details).
  • A Julia script may contain any number of functions, and all definitions will be externally visible when the file is loaded. Function definitions can be loaded from files outside the current working directory.
  • In Julia, reductions such as sum, prod, and max are performed over every element of an array when called with a single argument, as in sum(A), even if A has more than one dimension.
  • In Julia, parentheses must be used to call a function with zero arguments, like in rand().
  • Julia discourages the use of semicolons to end statements. The results of statements are not automatically printed (except at the interactive prompt), and lines of code do not need to end with semicolons. println or @printf can be used to print specific output.
  • In Julia, if A and B are arrays, logical comparison operations like A == B do not return an array of booleans. Instead, use A .== B, and similarly for the other boolean operators like <, >.
  • In Julia, the operators &, |, and ⊻ (xor) perform the bitwise operations equivalent to and, or, and xor respectively in MATLAB, and have precedence similar to Python's bitwise operators (unlike C). They can operate on scalars or element-wise across arrays and can be used to combine logical arrays, but note the difference in order of operations: parentheses may be required (e.g., to select elements of A equal to 1 or 2 use (A .== 1) .| (A .== 2)).
  • In Julia, the elements of a collection can be passed as arguments to a function using the splat operator ..., as in xs=[1,2]; f(xs...).
  • Julia's svd returns singular values as a vector instead of as a dense diagonal matrix.
  • In Julia, ... is not used to continue lines of code. Instead, incomplete expressions automatically continue onto the next line.
  • In both Julia and MATLAB, the variable ans is set to the value of the last expression issued in an interactive session. In Julia, unlike MATLAB, ans is not set when Julia code is run in non-interactive mode.
  • Julia's structs do not support dynamically adding fields at runtime, unlike MATLAB's classes. Instead, use a Dict.
  • In Julia each module has its own global scope/namespace, whereas in MATLAB there is just one global scope.
  • In MATLAB, an idiomatic way to remove unwanted values is to use logical indexing, like in the expression x(x>3) or in the statement x(x>3) = [] to modify x in-place. In contrast, Julia provides the higher order functions filter and filter!, allowing users to write filter(z->z>3, x) and filter!(z->z>3, x) as alternatives to the corresponding transliterations x[x.>3] and x = x[x.>3]. Using filter! reduces the use of temporary arrays.
  • The analogue of extracting (or "dereferencing") all elements of a cell array, e.g. in vertcat(A{:}) in MATLAB, is written using the splat operator in Julia, e.g. as vcat(A...).