versions of functions, which simply apply a given function f(x) to each element of an array A to yield a new array via f(A). This kind of syntax is convenient for data processing, but in other languages that use f(x): julia> g(x) = f(x) g (generic function with 1 method) julia> t = @async f(wait()); yield(); Now we add some new methods to f(x): julia> f(x::Int) = "definition for Int" f (generic function "definition for Int" julia> fetch(schedule(t, 1)) "original definition" julia> t = @async f(wait()); yield(); julia> fetch(schedule(t, 1)) "definition for Int"CHAPTER 12. METHODS 160 12.6 Design Patterns

versions of functions, which simply apply a given function f(x) to each element of an array A to yield a new array via f(A). This kind of syntax is convenient for data processing, but in other languages that use f(x): julia> g(x) = f(x) g (generic function with 1 method) julia> t = @async f(wait()); yield(); Now we add some new methods to f(x): julia> f(x::Int) = "definition for Int" f (generic function "definition for Int" julia> fetch(schedule(t, 1)) "original definition" julia> t = @async f(wait()); yield(); julia> fetch(schedule(t, 1)) "definition for Int"CHAPTER 12. METHODS 160 12.6 Design Patterns

versions of functions, which simply apply a given function f(x) to each element of an array A to yield a new array via f(A). This kind of syntax is convenient for data processing, but in other languages that use f(x): julia> g(x) = f(x) g (generic function with 1 method) julia> t = @async f(wait()); yield(); Now we add some new methods to f(x): julia> f(x::Int) = "definition for Int" f (generic function "definition for Int" julia> fetch(schedule(t, 1)) "original definition" julia> t = @async f(wait()); yield(); julia> fetch(schedule(t, 1)) "definition for Int" 13.6 Design Patterns with Parametric Methods

versions of functions, which simply apply a given function f(x) to each element of an array A to yield a new array via f(A). This kind of syntax is convenient for data processing, but in other languages that use f(x): julia> g(x) = f(x) g (generic function with 1 method) julia> t = @async f(wait()); yield(); Now we add some new methods to f(x): julia> f(x::Int) = "definition for Int" f (generic function "definition for Int" julia> fetch(schedule(t, 1)) "original definition" julia> t = @async f(wait()); yield(); julia> fetch(schedule(t, 1)) "definition for Int" 13.6 Design Patterns with Parametric Methods

versions of functions, which simply apply a given function f(x) to each element of an array A to yield a new array via f(A). This kind of syntax is convenient for data processing, but in other languages that use f(x): julia> g(x) = f(x) g (generic function with 1 method) julia> t = @async f(wait()); yield(); Now we add some new methods to f(x): julia> f(x::Int) = "definition for Int" f (generic function "definition for Int" julia> fetch(schedule(t, 1)) "original definition" julia> t = @async f(wait()); yield(); julia> fetch(schedule(t, 1)) "definition for Int" 13.6 Design Patterns with Parametric Methods

versions of functions, which simply apply a given function f(x) to each element of an array A to yield a new array via f(A). This kind of syntax is convenient for data processing, but in other languages that use f(x): julia> g(x) = f(x) g (generic function with 1 method) julia> t = @async f(wait()); yield(); Now we add some new methods to f(x): julia> f(x::Int) = "definition for Int" f (generic function "definition for Int" julia> fetch(schedule(t, 1)) "original definition" julia> t = @async f(wait()); yield(); julia> fetch(schedule(t, 1)) "definition for Int" 13.6 Design Patterns with Parametric Methods

versions of functions, which simply apply a given function f(x) to each element of an array A to yield a new array via f(A). This kind of syntax is convenient for data processing, but in other languages represents a single multidimensional index. When combined with other index- ing forms and iterators that yield CartesianIndexes, however, this can produce very elegant and efficient code. See Iteration below tasks should avoid performing high latency operations, and if they are long duration tasks, should yield frequently. By default Julia starts with one interactive thread reserved to run interactive tasks

versions of functions, which simply apply a given function f(x) to each element of an array A to yield a new array via f(A). This kind of syntax is convenient for data processing, but in other languages represents a single multidimensional index. When combined with other index- ing forms and iterators that yield CartesianIndexes, however, this can produce very elegant and efficient code. See Iteration below tasks should avoid performing high latency operations, and if they are long duration tasks, should yield frequently. By default Julia starts with one interactive thread reserved to run interactive tasks

versions of functions, which simply apply a given function f(x) to each element of an array A to yield a new array via f(A). This kind of syntax is convenient for data processing, but in other languages represents a single multidimensional index. When combined with other index- ing forms and iterators that yield CartesianIndexes, however, this can produce very elegant and efficient code. See Iteration below tasks should avoid performing high latency operations, and if they are long duration tasks, should yield frequently. By default Julia starts with one interactive thread reserved to run interactive tasks

versions of functions, which simply apply a given function f(x) to each element of an array A to yield a new array via f(A). This kind of syntax is convenient for data processing, but in other languages represents a single multidimensional index. When combined with other index- ing forms and iterators that yield CartesianIndexes, however, this can produce very elegant and efficient code. See Iteration below tasks should avoid performing high latency operations, and if they are long duration tasks, should yield frequently. By default Julia starts with one interactive thread reserved to run interactive tasks