Here is an example of the short form used to quote an arithmetic expression: julia> ex = :(a+b*c+1) The : character, followed by paired parentheses around a single statement of Julia code, produces an Expr object based on the enclosed code. The second syntactic purpose of the : character is to create expression objects without using the explicit Expr constructor. ![]() Sometimes extra parentheses around the argument to : are needed to avoid ambiguity in parsing: julia> :(:) In the context of an expression, symbols are used to indicate access to variables when an expression is evaluated, a symbol is replaced with the value bound to that symbol in the appropriate scope. Julia> Symbol("1foo") # `:1foo` would not work, as `1foo` is not a valid identifier The Symbol constructor takes any number of arguments and creates a new symbol by concatenating their string representations together: julia> :foo = Symbol("foo") The first form creates a Symbol, an interned string used as one building-block of expressions, from valid identifiers: julia> s = :foo The : character has two syntactic purposes in Julia. Here's an example illustrating the display on a nested Expr: julia> Meta.show_sexpr(ex3) The dump function provides indented and annotated display of Expr objects: julia> dump(ex2)Įxpr objects may also be nested: julia> ex3 = Meta.parse("(4 + 4) / 2")Īnother way to view expressions is with Meta.show_sexpr, which displays the S-expression form of a given Expr, which may look very familiar to users of Lisp. The key point here is that Julia code is internally represented as a data structure that is accessible from the language itself. The two expressions constructed above – by parsing and by direct construction – are equivalent: julia> ex1 = ex2 the expression arguments, which may be symbols, other expressions, or literal values:Įxpressions may also be constructed directly in prefix notation: julia> ex2 = Expr(:call, :+, 1, 1).A symbol is an interned string identifier (more discussion below). ![]() a Symbol identifying the kind of expression.The next step is to parse each string into an object called an expression, represented by the Julia type Expr: julia> ex1 = Meta.parse(prog) Program representationĮvery Julia program starts life as a string: julia> prog = "1 + 1" The best uses of metaprogramming often implement most of their functionality in runtime helper functions, striving to minimize the amount of code they generate. For manipulating Julia code, use the Expr data structure directly to avoid the complexity of how Julia syntax is parsed. It is almost never a good idea to use Meta.parse or convert an arbitrary string into Julia code. Metaprogramming should typically be used only when other approaches such as higher order functions and closures cannot be applied.Įval and defining new macros should be typically used as a last resort. For example, it can be surprisingly hard to get scope rules correct. Metaprogramming is a powerful tool, but it introduces complexity that can make code more difficult to understand. Instrumenting Julia with DTrace, and bpftrace.Reporting and analyzing crashes (segfaults).Static analyzer annotations for GC correctness in C code.Proper maintenance and care of multi-threading locks. ![]() printf() and stdio in the Julia runtime.Talking to the compiler (the :meta mechanism).High-level Overview of the Native-Code Generation Process.Noteworthy Differences from other Languages.Multi-processing and Distributed Computing.Mathematical Operations and Elementary Functions.
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