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Julia does not have an analog of MATLAB's clear function; once a name is defined in a Julia session (technically, in module Main), it is always present.
If memory usage is your concern, you can always replace objects with ones that consume less memory. For example, if A is a gigabyte-sized array that you no longer need, you can free the memory with A = nothing. The memory will be released the next time the garbage collector runs; you can force this to happen with gc(). Moreover, an attempt to use A will likely result in an error, because most methods are not defined on type Nothing.
Perhaps you've defined a type and then realize you need to add a new field. If you try this at the REPL, you get the error:
ERROR: invalid redefinition of constant MyType
Types in module Main cannot be redefined.
While this can be inconvenient when you are developing new code, there's an excellent workaround. Modules can be replaced by redefining them, and so if you wrap all your new code inside a module you can redefine types and constants. You can't import the type names into Main and then expect to be able to redefine them there, but you can use the module name to resolve the scope. In other words, while developing you might use a workflow something like this:
include("mynewcode.jl") # this defines a module MyModule
obj1 = MyModule.ObjConstructor(a, b)
obj2 = MyModule.somefunction(obj1)
# Got an error. Change something in "mynewcode.jl"
include("mynewcode.jl") # reload the module
obj1 = MyModule.ObjConstructor(a, b) # old objects are no longer valid, must reconstruct
obj2 = MyModule.somefunction(obj1) # this time it worked!
obj3 = MyModule.someotherfunction(obj2, c)
When a file is run as the main script using julia file.jl one might want to activate extra functionality like command line argument handling. A way to determine that a file is run in this fashion is to check if abspath(PROGRAM_FILE) == @__FILE__ is true.
No, you are not allowed to have a using or import statement inside a function. If you want to import a module but only use its symbols inside a specific function or set of functions, you have two options:
# ... refer to Foo symbols via Foo.baz ...
This loads the module Foo and defines a variable Foo that refers to the module, but does not import any of the other symbols from the module into the current namespace. You refer to the Foo symbols by their qualified names Foo.bar etc.
Wrap your function in a module:
# ... refer to Foo.baz as simply baz ....
This imports all the symbols from Foo, but only inside the module Bar.
It means that the type of the output is predictable from the types of the inputs. In particular, it means that the type of the output cannot vary depending on the values of the inputs.
The following code is not type-stable:
julia> function unstable(flag::Bool)
unstable (generic function with 1 method)
It returns either an Int or a Float64 depending on the value of its argument. Since Julia can't predict the return type of this function at compile-time, any computation that uses it will have to guard against both types possibly occurring, making generation of fast machine code difficult.
Certain operations make mathematical sense but result in errors:
ERROR: DomainError with -2.0:
sqrt will only return a complex result if called with a complex argument. Try sqrt(Complex(x)).
This behavior is an inconvenient consequence of the requirement for type-stability. In the case of sqrt, most users want sqrt(2.0) to give a real number, and would be unhappy if it produced the complex number 1.4142135623730951 + 0.0im. One could write the sqrt function to switch to a complex-valued output only when passed a negative number (which is what sqrt does in some other languages), but then the result would not be type-stable and the sqrt function would have poor performance.
In these and other cases, you can get the result you want by choosing an input type that conveys your willingness to accept an output type in which the result can be represented:
0.0 + 1.4142135623730951im.
You may prefer the release version of Julia if you are looking for a stable code base. Releases generally occur every 6 months, giving you a stable platform for writing code.
You may prefer the beta version of Julia if you don't mind being slightly behind the latest bugfixes and changes, but find the slightly faster rate of changes more appealing. Additionally, these binaries are tested before they are published to ensure they are fully functional.
You may prefer the nightly version of Julia if you want to take advantage of the latest updates to the language, and don't mind if the version available today occasionally doesn't actually work.
Finally, you may also consider building Julia from source for yourself. This option is mainly for those individuals who are comfortable at the command line, or interested in learning. If this describes you, you may also be interested in reading our guidelines for contributing.
Although the global variables are good enough to be trusted, the biggest issue with them is their value and types both changes frequently. Of course, this leads to code optimization problems. Later these issues make it very difficult for the users to test the code and make it OS independent. Thus, the better option is to use the local variables. Julia supports almost every local variable and let you come with similar quality even if all the variables used are local than global.
the fact is Julia has been equipped with a very large number of compiler techniques. This makes it possible for the programmers to deploy various techniques to keep up the pace simply. Many tasks that are actually difficult can be made simple with this approach.
Julia is widely preferred in both numerical computing and scientific computing mainly because of its performance. It can produce similar outcomes with short codes and thus make sure of best outcomes. A lot of tasks related to both scientific and numerical computing can easily be handled.
One of the best things about Julia is it has a very simple tool available for this and the same is @time. With this tool, performance can simply be measured accurately and without compromising with anything. It is possible for the programmers to even compile this tool for measuring quantities that are not possible with common methods.
No, it is actually different and sometimes this makes many programmers to think Julia is a very complex language which is not true.
Well, by avoiding the use of global variables, it has been seen that programmers can make sure of speed. However, it is not always necessary to work with all the programmers. It actually depends on the experience and programming skills of a professional that how he/she can enhance the code speed.
In Julia if a code is benchmarked or is very critical, the same should be assigned within a function. If not, there are various compatibility problems that can declare their presence. Also, benchmarked codes sometimes need to be kept in a separate container. It is possible to call them with a single command from the same.
toc () and tic() are the other functions that one can put equal to @time. However, they are not preferred by many programmers. The biggest reason for this is memory allocation problem. Both these functions consume more memory and can thus affect the performance up to a certain extent. With more memory, compatibility and performance related issue can commonly be seen.
The fact is Julia has been equipped with a very large number of supporting tools that the programmers can deploy when they want. In true sense, these tools are best in handling this task. One of the best available tools is Profiling. With this tool, it is possible for the programmers to monitor the quality of their code at the same time they are writing it. All the bugs can thus immediately be managed. Some programmers say it consumes time but actually it doesn’t.
Yes, it is allowed. However, programmers need to make it sure that they have annotated their type before actually using them in the machine.
To eliminate such an issue, users can proceed with ProfileView package with the help of which everything related to complexity can be managed in a reliable manner. Another package that can be considered is Lint. Programmers can always make sure of error elimination at the right time with this package. The best part is using these packages is not at all a big deal and there is no need for the programmers to have top-notch skills in packaging.
Yes, it is not possible but it needs a lot of efforts. Some programmers even call it a drawback in the Julia language. However, the fact is, Julia is a high level programming language and only experienced programmers are in a position to use them anywhere they want.
it is not possible in case of Julia. This is due to the fact that complier makes use of object types and not the values assigned when it comes to generating a code. This sometime enhances the overall length of the code.
Making changes in a code for cutting down its overall length and compatibility is not at all a big deal. It depends on the skills of a programmer whether he/she prefer making changes or start developing the entire code again. Sometimes modification consumes more time and efforts in Julia and therefore it is a wise option to proceed with writing code.
In Julia, it is possible for the programmers to assign more than one task to the code they develop. In other words a code can do different functions. However, this needs a lot of advanced programming skills. Most of the programmers prefer wrapping their code in a new function to keep up the pace in this matter. This is one of the major factors that have enhanced the overall application of Julia. Many programmers who are experienced now prefer Julia because of no other reason than this.
It is statically typed.
This is exactly what that makes Julia best in handling several tasks and the best part is it has been recognized as one of the best programming languages only because of these basic features.
This MATLAB contains a very large number of modules whereas Julia doesn’t. This makes MATLAB to have more and in fact, advanced applications. Also, MATLAB is widely preferred in electronic and electrical application most of the time (although it has other applications too), Julia is a computer programming language. Although MATLAB supports general programming, it is actually based on mathematical programming. Julia too can handle mathematical operations but not upto the extent MATLAB can.
Julia comes with some cons too. The biggest one is the limited library and the same is written in Julia only. This sometimes creates compatibility issues. Also, because of limited nature, programmers which are new have to take additional support again and again. The construction and predefining of objects is a very daunting task in Julia except for some basic operations. In addition to this, defining of functions is also limited.
Julia is having one of the major advantages over other languages when it comes to calling the C language. The fact is C functions can directly be called in Julia and without defining them in advance. There is actually no strict upper limit on this and C functions can be managed in an efficient manner.
It has some well-defined functions for this task. Also, there are some of the best and powerful capabilities for managing the processes.
There are a lot of Meta-programming facilities this programming language has been equipped with. Users can easily keep up the pace in every aspect and there are facilities related to making programming easy especially for the beginners.
It is basically an approach in which many computers work on a single problem. Julia can easily be deployed on this model.
Julia is an open source language due to which it possible for the programmers to have customized results. Many programmers have made changes into this language up to a good extent to get the best possible outcomes. Actually, open source approach makes it more flexible and with custom experiments, users are unable to explore it in a better way.
Julia has its own package manager and the same contributes a lot to this. All the packets and similar concepts can be managed reliably with them.
It stands for Common LISP Object system.
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