MATLAB.jl: Calling MATLAB in Julia Through MATLAB Engine

MATLAB

The MATLAB.jl package provides an interface for using MATLAB® from Julia using the MATLAB C api. In other words, this package allows users to call MATLAB functions within Julia, thus making it easy to interoperate with MATLAB from the Julia language.

You cannot use MATLAB.jl without having purchased and installed a copy of MATLAB® from MathWorks. This package is available free of charge and in no way replaces or alters any functionality of MathWorks's MATLAB product.

Overview

This package is composed of two aspects:

Creating and manipulating mxArrays (the data structure that MATLAB used to represent arrays and other kinds of data)

Communicating with MATLAB engine sessions

Warning:

MATLAB string arrays are not supported, and will throw an error exception. This also applies if they are nested within a MATLAB struct. This is a limitation of the MATLAB C api. The MATLAB function convertContainedStringsToChars may be used to facilitate conversion to a compatible format for use with MATLAB.jl.

Threading is also not supported within Julia when using the MATLAB.jl library.

Installation

Important: The procedure to setup this package consists of the following steps.

By default, MATLAB.jl uses the MATLAB installation with the greatest version number. To specify that a specific MATLAB installation should be used, set the environment variable MATLAB_ROOT.

Windows

For Matlab R2020a onwards, you should be able to go directly to step 2. If you encounter issues, run matlab -batch "comserver('register')" in the command prompt. For earlier versions of Matlab, start a command prompt as an administrator and enter matlab /regserver.

From Julia run: Pkg.add("MATLAB")

Linux

Make sure matlab is in executable path.

Make sure csh is installed. (Note: MATLAB for Linux relies on csh to open an engine session.)

To install csh in Debian/Ubuntu/Linux Mint, you may type in the following command in terminal:

sudo apt-get install csh

From Julia run: Pkg.add("MATLAB")

Mac OS X

Ensure that MATLAB is installed in /Applications (for example, if you are using MATLAB R2012b, you may add the following command to .profile: export MATLAB_HOME=/Applications/MATLAB_R2012b.app).

From Julia run: Pkg.add("MATLAB")

Usage

MxArray class

An instance of MxArray encapsulates a MATLAB variable. This package provides a series of functions to manipulate such instances.

Create MATLAB variables in Julia

One can use the function mxarray to create MATLAB variables (of type MxArray), as follows

mxarray(Float64, n)   # creates an n-by-1 MATLAB zero array of double valued type
mxarray(Int32, m, n)  # creates an m-by-n MATLAB zero array of int32 valued type
mxarray(Bool, m, n)   # creates a MATLAB logical array of size m-by-n

mxarray(Float64, (n1, n2, n3))  # creates a MATLAB array of size n1-by-n2-by-n3

mxcellarray(m, n)        # creates a MATLAB cell array
mxstruct("a", "b", "c")  # creates a MATLAB struct with given fields

You may also convert a Julia variable to MATLAB variable

a = rand(m, n)

x = mxarray(a)     # converts a to a MATLAB array
x = mxarray(1.2)   # converts a scalar 1.2 to a MATLAB variable

a = sprand(m, n, 0.1)
x = mxarray(a)     # converts a sparse matrix to a MATLAB sparse matrix

x = mxarray("abc") # converts a string to a MATLAB char array

x = mxarray(["a", 1, 2.3])  # converts a Julia array to a MATLAB cell array

x = mxarray(Dict("a"=>1, "b"=>"string", "c"=>[1,2,3])) # converts a Julia dictionary to a MATLAB struct

The function mxarray can also convert a compound type to a Julia struct:

struct S
    x::Float64
    y::Vector{Int32}
    z::Bool
end

s = S(1.2, Int32[1, 2], false)

x = mxarray(s)   # creates a MATLAB struct with three fields: x, y, z
xc = mxarray([s, s])  # creates a MATLAB cell array, each cell is a struct.
xs = mxstructarray([s, s])  # creates a MATLAB array of structs

Note: For safety, the conversation between MATLAB and Julia variables uses deep copy.

When you finish using a MATLAB variable, you may call delete to free the memory. But this is optional, it will be deleted when reclaimed by the garbage collector.

delete(x)

Note: if you put a MATLAB variable x to MATLAB engine session, then the MATLAB engine will take over the management of its life cylce, and you don't have to delete it explicitly.

Access MATLAB variables

You may access attributes and data of a MATLAB variable through the functions provided by this package.

# suppose x is of type MxArray
nrows(x)    # returns number of rows in x
ncols(x)    # returns number of columns in x
nelems(x)   # returns number of elements in x
ndims(x)    # returns number of dimensions in x
size(x)     # returns the size of x as a tuple
size(x, d)  # returns the size of x along a specific dimension

eltype(x)   # returns element type of x (in Julia Type)
elsize(x)   # return number of bytes per element

data_ptr(x)   # returns pointer to data (in Ptr{T}), where T is eltype(x)

# suppose s is a MATLAB struct
mxnfields(s)    # returns the number of fields in struct s

You may also make tests on a MATLAB variable.

is_double(x)   # returns whether x is a double array
is_sparse(x)   # returns whether x is sparse
is_complex(x)  # returns whether x is complex
is_cell(x)     # returns whether x is a cell array
is_struct(x)   # returns whether x is a struct
is_empty(x)    # returns whether x is empty

...            # there are many more there

Convert MATLAB variables to Julia

a = jarray(x)   # converts x to a Julia array
a = jvector(x)  # converts x to a Julia vector (1D array) when x is a vector
a = jscalar(x)  # converts x to a Julia scalar
a = jmatrix(x)  # converts x to a Julia matrix
a = jstring(x)  # converts x to a Julia string
a = jdict(x)    # converts a MATLAB struct to a Julia dictionary (using fieldnames as keys)

a = jvalue(x)  # converts x to a Julia value in default manner

Read/Write MAT Files

This package provides functions to manipulate MATLAB's mat files:

mf = MatFile(filename, mode)    # opens a MAT file using a specific mode, and returns a handle
mf = MatFile(filename)          # opens a MAT file for reading, equivalent to MatFile(filename, "r")
close(mf)                       # closes a MAT file.

get_mvariable(mf, name)   # gets a variable and returns an mxArray
get_variable(mf, name)    # gets a variable, but converts it to a Julia value using `jvalue`

put_variable(mf, name, v)   # puts a variable v to the MAT file
                            # v can be either an MxArray instance or normal variable
                            # If v is not an MxArray, it will be converted using `mxarray`

put_variables(mf; name1=v1, name2=v2, ...)  # put multiple variables using keyword arguments

variable_names(mf)   # get a vector of all variable names in a MAT file

There are also convenient functions that can get/put all variables in one call:

read_matfile(filename)    # returns a dictionary that maps each variable name
                          # to an MxArray instance

write_matfile(filename; name1=v1, name2=v2, ...)  # writes all variables given in the
                                                  # keyword argument list to a MAT file

Both read_matfile and write_matfile will close the MAT file handle before returning.

Examples:

struct S
    x::Float64
    y::Bool
    z::Vector{Float64}
end

write_matfile("test.mat";
    a = Int32[1 2 3; 4 5 6],
    b = [1.2, 3.4, 5.6, 7.8],
    c = [[0.0, 1.0], [1.0, 2.0], [1.0, 2.0, 3.0]],
    d = Dict("name"=>"MATLAB", "score"=>100.0),
    s = "abcde",
    ss = [S(1.0, true, [1., 2.]), S(2.0, false, [3., 4.])] )

This example will create a MAT file called test.mat, which contains six MATLAB variables:

• a: a 2-by-3 int32 array
• b: a 4-by-1 double array
• c: a 3-by-1 cell array, each cell contains a double vector
• d: a struct with two fields: name and score
• s: a string (i.e. char array)
• ss: an array of structs with two elements, and three fields: x, y, and z.

Use MATLAB Engine

Basic Use

To evaluate expressions in MATLAB, one may open a MATLAB engine session and communicate with it. There are three ways to call MATLAB from Julia:

• The mat"" custom string literal allows you to write MATLAB syntax inside Julia and use Julia variables directly from MATLAB via interpolation
• The eval_string evaluate a string containing MATLAB expressions (typically used with the helper macros @mget and @mput
• The mxcall function calls a given MATLAB function and returns the result

In general, the mat"" custom string literal is the preferred method to interact with the MATLAB engine.

Note: There can be multiple (reasonable) ways to convert a MATLAB variable to Julia array. For example, MATLAB represents a scalar using a 1-by-1 matrix. Here we have two choices in terms of converting such a matrix back to Julia: (1) convert to a scalar number, or (2) convert to a matrix of size 1-by-1.

The mat"" custom string literal

Text inside the mat"" custom string literal is in MATLAB syntax. Variables from Julia can be "interpolated" into MATLAB code by prefixing them with a dollar sign as you would interpolate them into an ordinary string.

using MATLAB

x = range(-10.0, stop=10.0, length=500)
mat"plot($x, sin($x))"  # evaluate a MATLAB function

y = range(2.0, stop=3.0, length=500)
mat"""
    $u = $x + $y
    $v = $x - $y
"""
@show u v               # u and v are accessible from Julia

As with ordinary string literals, you can also interpolate whole Julia expressions, e.g. mat"$(x[1]) = $(x[2]) + $(binomial(5, 2))".

eval_string

You may also use the eval_string function to evaluate MATLAB code as follows

eval_string("a = sum([1,2,3])")

The eval_string function also takes an optional argument that specifies which MATLAB session to evaluate the code in, e.g.

julia> s = MSession();
julia> eval_string(s, "a = sum([1,2,3])")
a =
     6

mxcall

You may also directly call a MATLAB function on Julia variables using mxcall:

x = -10.0:0.1:10.0
y = -10.0:0.1:10.0
xx, yy = mxcall(:meshgrid, 2, x, y)

Note: Since MATLAB functions behavior depends on the number of outputs, you have to specify the number of output arguments in mxcall as the second argument.

mxcall puts the input arguments to the MATLAB workspace (using mangled names), evaluates the function call in MATLAB, and retrieves the variable from the MATLAB session. This function is mainly provided for convenience. However, you should keep in mind that it may incur considerable overhead due to the communication between MATLAB and Julia domain.

@mget and @mput

The macro @mget can be used to extract the value of a MATLAB variable into Julia

julia> mat"a = 6"
julia> @mget a
6.0

The macro @mput can be used to translate a Julia variable into MATLAB

julia> x = [1,2,3]
julia> @mput x
julia> eval_string("y = sum(x)")
julia> @mget y
6.0
julia> @show y
a = 63.0

Calling custom MATLAB function

If the MATLAB function is not in the current directory, we need to first add it to the MATLAB path before calling through Julia:

mat"addpath('/path/to/folder')"
val = mat"myfunction($arg1, $arg2)"

For example, if there is a MATLAB file located at /path/to/folder with contents:

function [r,u] = test(x, y)
    r = x + y;
    u = x - y;
end

We can call this function as follows in Julia:

using MATLAB

x = range(-10.0, stop=10.0, length=500)
y = range(2.0, stop=3.0, length=500)

mat"addpath('/path/to/folder')"

r, u = mxcall(:test,2,x,y)

Viewing the MATLAB Session (Windows only)

To open an interactive window for the MATLAB session, use the command show_msession() and to hide the window, use hide_msession(). Warning: manually closing this window will result in an error or result in a segfault; it is advised that you only use the hide_msession() command to hide the interactive window.

Note that this feature only works on Windows.

# default
show_msession() # open the default MATLAB session interactive window
get_msession_visiblity() # get the session's visibility state
hide_msession() # hide the default MATLAB session interactive window

# similarily
s = MSession()
show_msession(s)
get_msession_visiblity(a)
hide_msession(s)

Advanced use of MATLAB Engines

This package provides a series of functions for users to control the communication with MATLAB sessions.

Here is an example:

s1 = MSession()    # creates a MATLAB session
s2 = MSession(0)   # creates a MATLAB session without recording output

x = rand(3, 4)
put_variable(s1, :x, x)  # put x to session s1

y = rand(2, 3)
put_variable(s2, :y, y)  # put y to session s2

eval_string(s1, "r = sin(x)")  # evaluate sin(x) in session s1
eval_string(s2, "r = sin(y)")  # evaluate sin(y) in session s2

r1_mx = get_mvariable(s1, :r)  # get r from s1
r2_mx = get_mvariable(s2, :r)  # get r from s2

r1 = jarray(r1_mx)
r2 = jarray(r2_mx)

# ... do other stuff on r1 and r2

close(s1)  # close session s1
close(s2)  # close session s2

Download Details: 

Author: JuliaInterop
Source Code: https://github.com/JuliaInterop/MATLAB.jl 
License: MIT license

#julia #matlab 

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Python Global Variables – How to Define a Global Variable Example

In this article, you will learn the basics of global variables.

To begin with, you will learn how to declare variables in Python and what the term 'variable scope' actually means.

Then, you will learn the differences between local and global variables and understand how to define global variables and how to use the global keyword.

What Are Variables in Python and How Do You Create Them? An Introduction for Beginners

You can think of variables as storage containers.

They are storage containers for holding data, information, and values that you would like to save in the computer's memory. You can then reference or even manipulate them at some point throughout the life of the program.

A variable has a symbolic name, and you can think of that name as the label on the storage container that acts as its identifier.

The variable name will be a reference and pointer to the data stored inside it. So, there is no need to remember the details of your data and information – you only need to reference the variable name that holds that data and information.

When giving a variable a name, make sure that it is descriptive of the data it holds. Variable names need to be clear and easily understandable both for your future self and the other developers you may be working with.

Now, let's see how to actually create a variable in Python.

When declaring variables in Python, you don't need to specify their data type.

For example, in the C programming language, you have to mention explicitly the type of data the variable will hold.

So, if you wanted to store your age which is an integer, or int type, this is what you would have to do in C:

#include <stdio.h>
 
int main(void)
{
  int age = 28;
  // 'int' is the data type
  // 'age' is the name 
  // 'age' is capable of holding integer values
  // positive/negative whole numbers or 0
  // '=' is the assignment operator
  // '28' is the value
}

However, this is how you would write the above in Python:

age = 28

#'age' is the variable name, or identifier
# '=' is the assignment operator
#'28' is the value assigned to the variable, so '28' is the value of 'age'

The variable name is always on the left-hand side, and the value you want to assign goes on the right-hand side after the assignment operator.

Keep in mind that you can change the values of variables throughout the life of a program:

my_age = 28

print(f"My age in 2022 is {my_age}.")

my_age = 29

print(f"My age in 2023 will be {my_age}.")

#output

#My age in 2022 is 28.
#My age in 2023 will be 29.

You keep the same variable name, my_age, but only change the value from 28 to 29.

What Does Variable Scope in Python Mean?

Variable scope refers to the parts and boundaries of a Python program where a variable is available, accessible, and visible.

There are four types of scope for Python variables, which are also known as the LEGB rule:

• Local,
• Enclosing,
• Global,
• Built-in.

For the rest of this article, you will focus on learning about creating variables with global scope, and you will understand the difference between the local and global variable scopes.

How to Create Variables With Local Scope in Python

Variables defined inside a function's body have local scope, which means they are accessible only within that particular function. In other words, they are 'local' to that function.

You can only access a local variable by calling the function.

def learn_to_code():
    #create local variable
    coding_website = "freeCodeCamp"
    print(f"The best place to learn to code is with {coding_website}!")

#call function
learn_to_code()


#output

#The best place to learn to code is with freeCodeCamp!

Look at what happens when I try to access that variable with a local scope from outside the function's body:

def learn_to_code():
    #create local variable
    coding_website = "freeCodeCamp"
    print(f"The best place to learn to code is with {coding_website}!")

#try to print local variable 'coding_website' from outside the function
print(coding_website)

#output

#NameError: name 'coding_website' is not defined

It raises a NameError because it is not 'visible' in the rest of the program. It is only 'visible' within the function where it was defined.

How to Create Variables With Global Scope in Python

When you define a variable outside a function, like at the top of the file, it has a global scope and it is known as a global variable.

A global variable is accessed from anywhere in the program.

You can use it inside a function's body, as well as access it from outside a function:

#create a global variable
coding_website = "freeCodeCamp"

def learn_to_code():
    #access the variable 'coding_website' inside the function
    print(f"The best place to learn to code is with {coding_website}!")

#call the function
learn_to_code()

#access the variable 'coding_website' from outside the function
print(coding_website)

#output

#The best place to learn to code is with freeCodeCamp!
#freeCodeCamp

What happens when there is a global and local variable, and they both have the same name?

#global variable
city = "Athens"

def travel_plans():
    #local variable with the same name as the global variable
    city = "London"
    print(f"I want to visit {city} next year!")

#call function - this will output the value of local variable
travel_plans()

#reference global variable - this will output the value of global variable
print(f"I want to visit {city} next year!")

#output

#I want to visit London next year!
#I want to visit Athens next year!

In the example above, maybe you were not expecting that specific output.

Maybe you thought that the value of city would change when I assigned it a different value inside the function.

Maybe you expected that when I referenced the global variable with the line print(f" I want to visit {city} next year!"), the output would be #I want to visit London next year! instead of #I want to visit Athens next year!.

However, when the function was called, it printed the value of the local variable.

Then, when I referenced the global variable outside the function, the value assigned to the global variable was printed.

They didn't interfere with one another.

That said, using the same variable name for global and local variables is not considered a best practice. Make sure that your variables don't have the same name, as you may get some confusing results when you run your program.

How to Use the global Keyword in Python

What if you have a global variable but want to change its value inside a function?

Look at what happens when I try to do that:

#global variable
city = "Athens"

def travel_plans():
    #First, this is like when I tried to access the global variable defined outside the function. 
    # This works fine on its own, as you saw earlier on.
    print(f"I want to visit {city} next year!")

    #However, when I then try to re-assign a different value to the global variable 'city' from inside the function,
    #after trying to print it,
    #it will throw an error
    city = "London"
    print(f"I want to visit {city} next year!")

#call function
travel_plans()

#output

#UnboundLocalError: local variable 'city' referenced before assignment

By default Python thinks you want to use a local variable inside a function.

So, when I first try to print the value of the variable and then re-assign a value to the variable I am trying to access, Python gets confused.

The way to change the value of a global variable inside a function is by using the global keyword:

#global variable
city = "Athens"

#print value of global variable
print(f"I want to visit {city} next year!")

def travel_plans():
    global city
    #print initial value of global variable
    print(f"I want to visit {city} next year!")
    #assign a different value to global variable from within function
    city = "London"
    #print new value
    print(f"I want to visit {city} next year!")

#call function
travel_plans()

#print value of global variable
print(f"I want to visit {city} next year!")

Use the global keyword before referencing it in the function, as you will get the following error: SyntaxError: name 'city' is used prior to global declaration.

Earlier, you saw that you couldn't access variables created inside functions since they have local scope.

The global keyword changes the visibility of variables declared inside functions.

def learn_to_code():
   global coding_website
   coding_website = "freeCodeCamp"
   print(f"The best place to learn to code is with {coding_website}!")

#call function
learn_to_code()

#access variable from within the function
print(coding_website)

#output

#The best place to learn to code is with freeCodeCamp!
#freeCodeCamp

Conclusion

And there you have it! You now know the basics of global variables in Python and can tell the differences between local and global variables.

I hope you found this article useful.

You'll start from the basics and learn in an interactive and beginner-friendly way. You'll also build five projects at the end to put into practice and help reinforce what you've learned.

Thanks for reading and happy coding!

Source: https://www.freecodecamp.org/news/python-global-variables-examples/

#python 

Variables Globales De Python: Cómo Definir Un Ejemplo De Variable Glob

En este artículo, aprenderá los conceptos básicos de las variables globales.

Para empezar, aprenderá cómo declarar variables en Python y qué significa realmente el término 'ámbito de variable'.

Luego, aprenderá las diferencias entre variables locales y globales y comprenderá cómo definir variables globales y cómo usar la globalpalabra clave.

¿Qué son las variables en Python y cómo se crean? Una introducción para principiantes

Puede pensar en las variables como contenedores de almacenamiento .

Son contenedores de almacenamiento para almacenar datos, información y valores que le gustaría guardar en la memoria de la computadora. Luego puede hacer referencia a ellos o incluso manipularlos en algún momento a lo largo de la vida del programa.

Una variable tiene un nombre simbólico y puede pensar en ese nombre como la etiqueta en el contenedor de almacenamiento que actúa como su identificador.

El nombre de la variable será una referencia y un puntero a los datos almacenados en su interior. Por lo tanto, no es necesario recordar los detalles de sus datos e información; solo necesita hacer referencia al nombre de la variable que contiene esos datos e información.

Al dar un nombre a una variable, asegúrese de que sea descriptivo de los datos que contiene. Los nombres de las variables deben ser claros y fácilmente comprensibles tanto para usted en el futuro como para los otros desarrolladores con los que puede estar trabajando.

Ahora, veamos cómo crear una variable en Python.

Al declarar variables en Python, no necesita especificar su tipo de datos.

Por ejemplo, en el lenguaje de programación C, debe mencionar explícitamente el tipo de datos que contendrá la variable.

Entonces, si quisiera almacenar su edad, que es un número entero, o inttipo, esto es lo que tendría que hacer en C:

#include <stdio.h>
 
int main(void)
{
  int age = 28;
  // 'int' is the data type
  // 'age' is the name 
  // 'age' is capable of holding integer values
  // positive/negative whole numbers or 0
  // '=' is the assignment operator
  // '28' is the value
}

Sin embargo, así es como escribirías lo anterior en Python:

age = 28

#'age' is the variable name, or identifier
# '=' is the assignment operator
#'28' is the value assigned to the variable, so '28' is the value of 'age'

El nombre de la variable siempre está en el lado izquierdo y el valor que desea asignar va en el lado derecho después del operador de asignación.

Tenga en cuenta que puede cambiar los valores de las variables a lo largo de la vida de un programa:

my_age = 28

print(f"My age in 2022 is {my_age}.")

my_age = 29

print(f"My age in 2023 will be {my_age}.")

#output

#My age in 2022 is 28.
#My age in 2023 will be 29.

Mantienes el mismo nombre de variable my_age, pero solo cambias el valor de 28a 29.

¿Qué significa el alcance variable en Python?

El alcance de la variable se refiere a las partes y los límites de un programa de Python donde una variable está disponible, accesible y visible.

Hay cuatro tipos de alcance para las variables de Python, que también se conocen como la regla LEGB :

• local ,
• Encerrando ,
• globales ,
• Incorporado .

En el resto de este artículo, se centrará en aprender a crear variables con alcance global y comprenderá la diferencia entre los alcances de variables locales y globales.

Cómo crear variables con alcance local en Python

Las variables definidas dentro del cuerpo de una función tienen alcance local , lo que significa que solo se puede acceder a ellas dentro de esa función en particular. En otras palabras, son 'locales' para esa función.

Solo puede acceder a una variable local llamando a la función.

def learn_to_code():
    #create local variable
    coding_website = "freeCodeCamp"
    print(f"The best place to learn to code is with {coding_website}!")

#call function
learn_to_code()


#output

#The best place to learn to code is with freeCodeCamp!

Mire lo que sucede cuando trato de acceder a esa variable con un alcance local desde fuera del cuerpo de la función:

def learn_to_code():
    #create local variable
    coding_website = "freeCodeCamp"
    print(f"The best place to learn to code is with {coding_website}!")

#try to print local variable 'coding_website' from outside the function
print(coding_website)

#output

#NameError: name 'coding_website' is not defined

Plantea un NameErrorporque no es 'visible' en el resto del programa. Solo es 'visible' dentro de la función donde se definió.

Cómo crear variables con alcance global en Python

Cuando define una variable fuera de una función, como en la parte superior del archivo, tiene un alcance global y se conoce como variable global.

Se accede a una variable global desde cualquier parte del programa.

Puede usarlo dentro del cuerpo de una función, así como acceder desde fuera de una función:

#create a global variable
coding_website = "freeCodeCamp"

def learn_to_code():
    #access the variable 'coding_website' inside the function
    print(f"The best place to learn to code is with {coding_website}!")

#call the function
learn_to_code()

#access the variable 'coding_website' from outside the function
print(coding_website)

#output

#The best place to learn to code is with freeCodeCamp!
#freeCodeCamp

¿Qué sucede cuando hay una variable global y local, y ambas tienen el mismo nombre?

#global variable
city = "Athens"

def travel_plans():
    #local variable with the same name as the global variable
    city = "London"
    print(f"I want to visit {city} next year!")

#call function - this will output the value of local variable
travel_plans()

#reference global variable - this will output the value of global variable
print(f"I want to visit {city} next year!")

#output

#I want to visit London next year!
#I want to visit Athens next year!

En el ejemplo anterior, tal vez no esperaba ese resultado específico.

Tal vez pensaste que el valor de citycambiaría cuando le asignara un valor diferente dentro de la función.

Tal vez esperabas que cuando hice referencia a la variable global con la línea print(f" I want to visit {city} next year!"), la salida sería en #I want to visit London next year!lugar de #I want to visit Athens next year!.

Sin embargo, cuando se llamó a la función, imprimió el valor de la variable local.

Luego, cuando hice referencia a la variable global fuera de la función, se imprimió el valor asignado a la variable global.

No interfirieron entre sí.

Dicho esto, usar el mismo nombre de variable para variables globales y locales no se considera una buena práctica. Asegúrese de que sus variables no tengan el mismo nombre, ya que puede obtener algunos resultados confusos cuando ejecute su programa.

Cómo usar la globalpalabra clave en Python

¿Qué sucede si tiene una variable global pero desea cambiar su valor dentro de una función?

Mira lo que sucede cuando trato de hacer eso:

#global variable
city = "Athens"

def travel_plans():
    #First, this is like when I tried to access the global variable defined outside the function. 
    # This works fine on its own, as you saw earlier on.
    print(f"I want to visit {city} next year!")

    #However, when I then try to re-assign a different value to the global variable 'city' from inside the function,
    #after trying to print it,
    #it will throw an error
    city = "London"
    print(f"I want to visit {city} next year!")

#call function
travel_plans()

#output

#UnboundLocalError: local variable 'city' referenced before assignment

Por defecto, Python piensa que quieres usar una variable local dentro de una función.

Entonces, cuando intento imprimir el valor de la variable por primera vez y luego reasignar un valor a la variable a la que intento acceder, Python se confunde.

La forma de cambiar el valor de una variable global dentro de una función es usando la globalpalabra clave:

#global variable
city = "Athens"

#print value of global variable
print(f"I want to visit {city} next year!")

def travel_plans():
    global city
    #print initial value of global variable
    print(f"I want to visit {city} next year!")
    #assign a different value to global variable from within function
    city = "London"
    #print new value
    print(f"I want to visit {city} next year!")

#call function
travel_plans()

#print value of global variable
print(f"I want to visit {city} next year!")

Utilice la globalpalabra clave antes de hacer referencia a ella en la función, ya que obtendrá el siguiente error: SyntaxError: name 'city' is used prior to global declaration.

Anteriormente, vio que no podía acceder a las variables creadas dentro de las funciones ya que tienen un alcance local.

La globalpalabra clave cambia la visibilidad de las variables declaradas dentro de las funciones.

def learn_to_code():
   global coding_website
   coding_website = "freeCodeCamp"
   print(f"The best place to learn to code is with {coding_website}!")

#call function
learn_to_code()

#access variable from within the function
print(coding_website)

#output

#The best place to learn to code is with freeCodeCamp!
#freeCodeCamp

Conclusión

¡Y ahí lo tienes! Ahora conoce los conceptos básicos de las variables globales en Python y puede distinguir las diferencias entre las variables locales y globales.

Espero que hayas encontrado útil este artículo.

Comenzará desde lo básico y aprenderá de una manera interactiva y amigable para principiantes. También construirá cinco proyectos al final para poner en práctica y ayudar a reforzar lo que ha aprendido.

¡Gracias por leer y feliz codificación!

Fuente: https://www.freecodecamp.org/news/python-global-variables-examples/

#python 

Pythonグローバル変数–グローバル変数の例を定義する方法

この記事では、グローバル変数の基本を学びます。

まず、Pythonで変数を宣言する方法と、「変数スコープ」という用語が実際に何を意味するかを学習します。

次に、ローカル変数とグローバル変数の違いを学び、グローバル変数の定義方法とglobalキーワードの使用方法を理解します。

Pythonの変数とは何ですか？どのように作成しますか？初心者のための紹介

変数はストレージコンテナと考えることができます。

これらは、コンピュータのメモリに保存したいデータ、情報、および値を保持するためのストレージコンテナです。その後、プログラムの存続期間中のある時点でそれらを参照したり、操作したりすることもできます。

変数にはシンボリック名があり、その名前は、その識別子として機能するストレージコンテナのラベルと考えることができます。

変数名は、その中に格納されているデータへの参照とポインターになります。したがって、データと情報の詳細を覚えておく必要はありません。そのデータと情報を保持する変数名を参照するだけで済みます。

変数に名前を付けるときは、変数が保持するデータを説明していることを確認してください。変数名は、将来の自分自身と一緒に作業する可能性のある他の開発者の両方にとって、明確で簡単に理解できる必要があります。

それでは、Pythonで実際に変数を作成する方法を見てみましょう。

Pythonで変数を宣言するときは、データ型を指定する必要はありません。

たとえば、Cプログラミング言語では、変数が保持するデータの型を明示的に指定する必要があります。

したがって、整数またはint型である年齢を格納したい場合、これはCで行う必要があることです。

#include <stdio.h>
 
int main(void)
{
  int age = 28;
  // 'int' is the data type
  // 'age' is the name 
  // 'age' is capable of holding integer values
  // positive/negative whole numbers or 0
  // '=' is the assignment operator
  // '28' is the value
}

ただし、これはPythonで上記を記述する方法です。

age = 28

#'age' is the variable name, or identifier
# '=' is the assignment operator
#'28' is the value assigned to the variable, so '28' is the value of 'age'

変数名は常に左側にあり、代入する値は代入演算子の後に右側に配置されます。

プログラムの存続期間中、変数の値を変更できることに注意してください。

my_age = 28

print(f"My age in 2022 is {my_age}.")

my_age = 29

print(f"My age in 2023 will be {my_age}.")

#output

#My age in 2022 is 28.
#My age in 2023 will be 29.

同じ変数名を保持しますが、値をからにmy_age変更するだけです。2829

Pythonの可変スコープとはどういう意味ですか？

変数スコープとは、変数が利用可能で、アクセス可能で、表示可能なPythonプログラムの部分と境界を指します。

Python変数のスコープには4つのタイプがあり、 LEGBルールとも呼ばれます。

• 局所、
• 囲み、
• グローバル、
• ビルトイン。

この記事の残りの部分では、グローバルスコープを使用した変数の作成について学習することに焦点を当て、ローカル変数スコープとグローバル変数スコープの違いを理解します。

Pythonでローカルスコープを使用して変数を作成する方法

関数の本体内で定義された変数にはローカルスコープがあります。つまり、その特定の関数内でのみアクセスできます。言い換えれば、それらはその関数に対して「ローカル」です。

ローカル変数にアクセスするには、関数を呼び出す必要があります。

def learn_to_code():
    #create local variable
    coding_website = "freeCodeCamp"
    print(f"The best place to learn to code is with {coding_website}!")

#call function
learn_to_code()


#output

#The best place to learn to code is with freeCodeCamp!

関数の本体の外部からローカルスコープを使用してその変数にアクセスしようとするとどうなるかを見てください。

def learn_to_code():
    #create local variable
    coding_website = "freeCodeCamp"
    print(f"The best place to learn to code is with {coding_website}!")

#try to print local variable 'coding_website' from outside the function
print(coding_website)

#output

#NameError: name 'coding_website' is not defined

NameErrorプログラムの残りの部分では「表示」されないため、aが発生します。定義された関数内でのみ「表示」されます。

Pythonでグローバルスコープを使用して変数を作成する方法

ファイルの先頭など、関数の外部で変数を定義すると、その変数はグローバルスコープを持ち、グローバル変数と呼ばれます。

グローバル変数は、プログラムのどこからでもアクセスできます。

関数の本体内で使用することも、関数の外部からアクセスすることもできます。

#create a global variable
coding_website = "freeCodeCamp"

def learn_to_code():
    #access the variable 'coding_website' inside the function
    print(f"The best place to learn to code is with {coding_website}!")

#call the function
learn_to_code()

#access the variable 'coding_website' from outside the function
print(coding_website)

#output

#The best place to learn to code is with freeCodeCamp!
#freeCodeCamp

グローバル変数とローカル変数があり、両方が同じ名前の場合はどうなりますか？

#global variable
city = "Athens"

def travel_plans():
    #local variable with the same name as the global variable
    city = "London"
    print(f"I want to visit {city} next year!")

#call function - this will output the value of local variable
travel_plans()

#reference global variable - this will output the value of global variable
print(f"I want to visit {city} next year!")

#output

#I want to visit London next year!
#I want to visit Athens next year!

上記の例では、その特定の出力を期待していなかった可能性があります。

city関数内で別の値を割り当てたときに、の値が変わると思ったかもしれません。

たぶん、私が行でグローバル変数を参照したときprint(f" I want to visit {city} next year!")、出力は#I want to visit London next year!の代わりになると予想しました#I want to visit Athens next year!。

ただし、関数が呼び出されると、ローカル変数の値が出力されます。

次に、関数の外部でグローバル変数を参照すると、グローバル変数に割り当てられた値が出力されました。

彼らはお互いに干渉しませんでした。

ただし、グローバル変数とローカル変数に同じ変数名を使用することは、ベストプラクティスとは見なされません。プログラムを実行すると混乱する結果が生じる可能性があるため、変数の名前が同じでないことを確認してください。

Pythonでキーワードを使用する方法global

グローバル変数があり、関数内でその値を変更したい場合はどうなりますか？

私がそれをしようとすると何が起こるか見てください：

#global variable
city = "Athens"

def travel_plans():
    #First, this is like when I tried to access the global variable defined outside the function. 
    # This works fine on its own, as you saw earlier on.
    print(f"I want to visit {city} next year!")

    #However, when I then try to re-assign a different value to the global variable 'city' from inside the function,
    #after trying to print it,
    #it will throw an error
    city = "London"
    print(f"I want to visit {city} next year!")

#call function
travel_plans()

#output

#UnboundLocalError: local variable 'city' referenced before assignment

デフォルトでは、Pythonは関数内でローカル変数を使用したいと考えています。

そのため、最初に変数の値を出力してから、アクセスしようとしている変数に値を再割り当てしようとすると、Pythonが混乱します。

関数内のグローバル変数の値を変更する方法は、次のglobalキーワードを使用することです。

#global variable
city = "Athens"

#print value of global variable
print(f"I want to visit {city} next year!")

def travel_plans():
    global city
    #print initial value of global variable
    print(f"I want to visit {city} next year!")
    #assign a different value to global variable from within function
    city = "London"
    #print new value
    print(f"I want to visit {city} next year!")

#call function
travel_plans()

#print value of global variable
print(f"I want to visit {city} next year!")

global次のエラーが発生するため、関数でキーワードを参照する前にキーワードを使用してくださいSyntaxError: name 'city' is used prior to global declaration。

以前、関数内で作成された変数はローカルスコープを持っているため、それらにアクセスできないことを確認しました。

globalキーワードは、関数内で宣言された変数の可視性を変更します。

def learn_to_code():
   global coding_website
   coding_website = "freeCodeCamp"
   print(f"The best place to learn to code is with {coding_website}!")

#call function
learn_to_code()

#access variable from within the function
print(coding_website)

#output

#The best place to learn to code is with freeCodeCamp!
#freeCodeCamp

結論

そして、あなたはそれを持っています！これで、Pythonのグローバル変数の基本を理解し、ローカル変数とグローバル変数の違いを理解できます。

この記事がお役に立てば幸いです。

基本から始めて、インタラクティブで初心者に優しい方法で学びます。また、最後に5つのプロジェクトを構築して実践し、学んだことを強化するのに役立てます。

読んでくれてありがとう、そして幸せなコーディング！

ソース：https ：//www.freecodecamp.org/news/python-global-variables-examples/

#python 

MATLAB.jl: Calling MATLAB in Julia Through MATLAB Engine

MATLAB

The MATLAB.jl package provides an interface for using MATLAB® from Julia using the MATLAB C api. In other words, this package allows users to call MATLAB functions within Julia, thus making it easy to interoperate with MATLAB from the Julia language.

You cannot use MATLAB.jl without having purchased and installed a copy of MATLAB® from MathWorks. This package is available free of charge and in no way replaces or alters any functionality of MathWorks's MATLAB product.

Overview

This package is composed of two aspects:

Creating and manipulating mxArrays (the data structure that MATLAB used to represent arrays and other kinds of data)

Communicating with MATLAB engine sessions

Warning:

MATLAB string arrays are not supported, and will throw an error exception. This also applies if they are nested within a MATLAB struct. This is a limitation of the MATLAB C api. The MATLAB function convertContainedStringsToChars may be used to facilitate conversion to a compatible format for use with MATLAB.jl.

Threading is also not supported within Julia when using the MATLAB.jl library.

Installation

Important: The procedure to setup this package consists of the following steps.

By default, MATLAB.jl uses the MATLAB installation with the greatest version number. To specify that a specific MATLAB installation should be used, set the environment variable MATLAB_ROOT.

Windows

For Matlab R2020a onwards, you should be able to go directly to step 2. If you encounter issues, run matlab -batch "comserver('register')" in the command prompt. For earlier versions of Matlab, start a command prompt as an administrator and enter matlab /regserver.

From Julia run: Pkg.add("MATLAB")

Linux

Make sure matlab is in executable path.

Make sure csh is installed. (Note: MATLAB for Linux relies on csh to open an engine session.)

To install csh in Debian/Ubuntu/Linux Mint, you may type in the following command in terminal:

sudo apt-get install csh

From Julia run: Pkg.add("MATLAB")

Mac OS X

Ensure that MATLAB is installed in /Applications (for example, if you are using MATLAB R2012b, you may add the following command to .profile: export MATLAB_HOME=/Applications/MATLAB_R2012b.app).

From Julia run: Pkg.add("MATLAB")

Usage

MxArray class

An instance of MxArray encapsulates a MATLAB variable. This package provides a series of functions to manipulate such instances.

Create MATLAB variables in Julia

One can use the function mxarray to create MATLAB variables (of type MxArray), as follows

mxarray(Float64, n)   # creates an n-by-1 MATLAB zero array of double valued type
mxarray(Int32, m, n)  # creates an m-by-n MATLAB zero array of int32 valued type
mxarray(Bool, m, n)   # creates a MATLAB logical array of size m-by-n

mxarray(Float64, (n1, n2, n3))  # creates a MATLAB array of size n1-by-n2-by-n3

mxcellarray(m, n)        # creates a MATLAB cell array
mxstruct("a", "b", "c")  # creates a MATLAB struct with given fields

You may also convert a Julia variable to MATLAB variable

a = rand(m, n)

x = mxarray(a)     # converts a to a MATLAB array
x = mxarray(1.2)   # converts a scalar 1.2 to a MATLAB variable

a = sprand(m, n, 0.1)
x = mxarray(a)     # converts a sparse matrix to a MATLAB sparse matrix

x = mxarray("abc") # converts a string to a MATLAB char array

x = mxarray(["a", 1, 2.3])  # converts a Julia array to a MATLAB cell array

x = mxarray(Dict("a"=>1, "b"=>"string", "c"=>[1,2,3])) # converts a Julia dictionary to a MATLAB struct

The function mxarray can also convert a compound type to a Julia struct:

struct S
    x::Float64
    y::Vector{Int32}
    z::Bool
end

s = S(1.2, Int32[1, 2], false)

x = mxarray(s)   # creates a MATLAB struct with three fields: x, y, z
xc = mxarray([s, s])  # creates a MATLAB cell array, each cell is a struct.
xs = mxstructarray([s, s])  # creates a MATLAB array of structs

Note: For safety, the conversation between MATLAB and Julia variables uses deep copy.

When you finish using a MATLAB variable, you may call delete to free the memory. But this is optional, it will be deleted when reclaimed by the garbage collector.

delete(x)

Note: if you put a MATLAB variable x to MATLAB engine session, then the MATLAB engine will take over the management of its life cylce, and you don't have to delete it explicitly.

Access MATLAB variables

You may access attributes and data of a MATLAB variable through the functions provided by this package.

# suppose x is of type MxArray
nrows(x)    # returns number of rows in x
ncols(x)    # returns number of columns in x
nelems(x)   # returns number of elements in x
ndims(x)    # returns number of dimensions in x
size(x)     # returns the size of x as a tuple
size(x, d)  # returns the size of x along a specific dimension

eltype(x)   # returns element type of x (in Julia Type)
elsize(x)   # return number of bytes per element

data_ptr(x)   # returns pointer to data (in Ptr{T}), where T is eltype(x)

# suppose s is a MATLAB struct
mxnfields(s)    # returns the number of fields in struct s

You may also make tests on a MATLAB variable.

is_double(x)   # returns whether x is a double array
is_sparse(x)   # returns whether x is sparse
is_complex(x)  # returns whether x is complex
is_cell(x)     # returns whether x is a cell array
is_struct(x)   # returns whether x is a struct
is_empty(x)    # returns whether x is empty

...            # there are many more there

Convert MATLAB variables to Julia

a = jarray(x)   # converts x to a Julia array
a = jvector(x)  # converts x to a Julia vector (1D array) when x is a vector
a = jscalar(x)  # converts x to a Julia scalar
a = jmatrix(x)  # converts x to a Julia matrix
a = jstring(x)  # converts x to a Julia string
a = jdict(x)    # converts a MATLAB struct to a Julia dictionary (using fieldnames as keys)

a = jvalue(x)  # converts x to a Julia value in default manner

Read/Write MAT Files

This package provides functions to manipulate MATLAB's mat files:

mf = MatFile(filename, mode)    # opens a MAT file using a specific mode, and returns a handle
mf = MatFile(filename)          # opens a MAT file for reading, equivalent to MatFile(filename, "r")
close(mf)                       # closes a MAT file.

get_mvariable(mf, name)   # gets a variable and returns an mxArray
get_variable(mf, name)    # gets a variable, but converts it to a Julia value using `jvalue`

put_variable(mf, name, v)   # puts a variable v to the MAT file
                            # v can be either an MxArray instance or normal variable
                            # If v is not an MxArray, it will be converted using `mxarray`

put_variables(mf; name1=v1, name2=v2, ...)  # put multiple variables using keyword arguments

variable_names(mf)   # get a vector of all variable names in a MAT file

There are also convenient functions that can get/put all variables in one call:

read_matfile(filename)    # returns a dictionary that maps each variable name
                          # to an MxArray instance

write_matfile(filename; name1=v1, name2=v2, ...)  # writes all variables given in the
                                                  # keyword argument list to a MAT file

Both read_matfile and write_matfile will close the MAT file handle before returning.

Examples:

struct S
    x::Float64
    y::Bool
    z::Vector{Float64}
end

write_matfile("test.mat";
    a = Int32[1 2 3; 4 5 6],
    b = [1.2, 3.4, 5.6, 7.8],
    c = [[0.0, 1.0], [1.0, 2.0], [1.0, 2.0, 3.0]],
    d = Dict("name"=>"MATLAB", "score"=>100.0),
    s = "abcde",
    ss = [S(1.0, true, [1., 2.]), S(2.0, false, [3., 4.])] )

This example will create a MAT file called test.mat, which contains six MATLAB variables:

• a: a 2-by-3 int32 array
• b: a 4-by-1 double array
• c: a 3-by-1 cell array, each cell contains a double vector
• d: a struct with two fields: name and score
• s: a string (i.e. char array)
• ss: an array of structs with two elements, and three fields: x, y, and z.

Use MATLAB Engine

Basic Use

To evaluate expressions in MATLAB, one may open a MATLAB engine session and communicate with it. There are three ways to call MATLAB from Julia:

• The mat"" custom string literal allows you to write MATLAB syntax inside Julia and use Julia variables directly from MATLAB via interpolation
• The eval_string evaluate a string containing MATLAB expressions (typically used with the helper macros @mget and @mput
• The mxcall function calls a given MATLAB function and returns the result

In general, the mat"" custom string literal is the preferred method to interact with the MATLAB engine.

Note: There can be multiple (reasonable) ways to convert a MATLAB variable to Julia array. For example, MATLAB represents a scalar using a 1-by-1 matrix. Here we have two choices in terms of converting such a matrix back to Julia: (1) convert to a scalar number, or (2) convert to a matrix of size 1-by-1.

The mat"" custom string literal

Text inside the mat"" custom string literal is in MATLAB syntax. Variables from Julia can be "interpolated" into MATLAB code by prefixing them with a dollar sign as you would interpolate them into an ordinary string.

using MATLAB

x = range(-10.0, stop=10.0, length=500)
mat"plot($x, sin($x))"  # evaluate a MATLAB function

y = range(2.0, stop=3.0, length=500)
mat"""
    $u = $x + $y
    $v = $x - $y
"""
@show u v               # u and v are accessible from Julia

As with ordinary string literals, you can also interpolate whole Julia expressions, e.g. mat"$(x[1]) = $(x[2]) + $(binomial(5, 2))".

eval_string

You may also use the eval_string function to evaluate MATLAB code as follows

eval_string("a = sum([1,2,3])")

The eval_string function also takes an optional argument that specifies which MATLAB session to evaluate the code in, e.g.

julia> s = MSession();
julia> eval_string(s, "a = sum([1,2,3])")
a =
     6

mxcall

You may also directly call a MATLAB function on Julia variables using mxcall:

x = -10.0:0.1:10.0
y = -10.0:0.1:10.0
xx, yy = mxcall(:meshgrid, 2, x, y)

Note: Since MATLAB functions behavior depends on the number of outputs, you have to specify the number of output arguments in mxcall as the second argument.

mxcall puts the input arguments to the MATLAB workspace (using mangled names), evaluates the function call in MATLAB, and retrieves the variable from the MATLAB session. This function is mainly provided for convenience. However, you should keep in mind that it may incur considerable overhead due to the communication between MATLAB and Julia domain.

@mget and @mput

The macro @mget can be used to extract the value of a MATLAB variable into Julia

julia> mat"a = 6"
julia> @mget a
6.0

The macro @mput can be used to translate a Julia variable into MATLAB

julia> x = [1,2,3]
julia> @mput x
julia> eval_string("y = sum(x)")
julia> @mget y
6.0
julia> @show y
a = 63.0

Calling custom MATLAB function

If the MATLAB function is not in the current directory, we need to first add it to the MATLAB path before calling through Julia:

mat"addpath('/path/to/folder')"
val = mat"myfunction($arg1, $arg2)"

For example, if there is a MATLAB file located at /path/to/folder with contents:

function [r,u] = test(x, y)
    r = x + y;
    u = x - y;
end

We can call this function as follows in Julia:

using MATLAB

x = range(-10.0, stop=10.0, length=500)
y = range(2.0, stop=3.0, length=500)

mat"addpath('/path/to/folder')"

r, u = mxcall(:test,2,x,y)

Viewing the MATLAB Session (Windows only)

To open an interactive window for the MATLAB session, use the command show_msession() and to hide the window, use hide_msession(). Warning: manually closing this window will result in an error or result in a segfault; it is advised that you only use the hide_msession() command to hide the interactive window.

Note that this feature only works on Windows.

# default
show_msession() # open the default MATLAB session interactive window
get_msession_visiblity() # get the session's visibility state
hide_msession() # hide the default MATLAB session interactive window

# similarily
s = MSession()
show_msession(s)
get_msession_visiblity(a)
hide_msession(s)

Advanced use of MATLAB Engines

This package provides a series of functions for users to control the communication with MATLAB sessions.

Here is an example:

s1 = MSession()    # creates a MATLAB session
s2 = MSession(0)   # creates a MATLAB session without recording output

x = rand(3, 4)
put_variable(s1, :x, x)  # put x to session s1

y = rand(2, 3)
put_variable(s2, :y, y)  # put y to session s2

eval_string(s1, "r = sin(x)")  # evaluate sin(x) in session s1
eval_string(s2, "r = sin(y)")  # evaluate sin(y) in session s2

r1_mx = get_mvariable(s1, :r)  # get r from s1
r2_mx = get_mvariable(s2, :r)  # get r from s2

r1 = jarray(r1_mx)
r2 = jarray(r2_mx)

# ... do other stuff on r1 and r2

close(s1)  # close session s1
close(s2)  # close session s2

Download Details: 

Author: JuliaInterop
Source Code: https://github.com/JuliaInterop/MATLAB.jl 
License: MIT license

#julia #matlab 

MATDaemon.jl: Calling Julia from MATLAB

In this tutorial, we'll learn about MATDaemon.jl, calling Julia from MATLAB. Calling MATLAB code from Julia via the C API has been supported for many years via MATLAB.jl

MATDaemon.jl

Call Julia from MATLAB using a Julia daemon launched by DaemonMode.jl.

Quickstart

Use the MATLAB function jlcall.m to call Julia from MATLAB:

>> jlcall('sort', {rand(2,5)}, struct('dims', int64(2)))

ans =

    0.1270    0.2785    0.6324    0.8147    0.9575
    0.0975    0.5469    0.9058    0.9134    0.9649

The positional arguments passed to jlcall.m are:

1. The Julia function to call, given as a MATLAB char array. This can be any Julia expression which evaluates to a function. For example, 'a=2; b=3; x -> a*x+b'. For convenience, the empty string '' is interpreted as '(args...; kwargs...) -> nothing', returning nothing for any inputs. Note: expressions are wrapped in a let block and evaluated in the global scope
2. Positional arguments, given as a MATLAB cell array. For example, args = {arg1, arg2, ...}
3. Keyword arguments, given as a MATLAB struct. For example, kwargs = struct('key1', value1, 'key2', value2, ...)

The first time jlcall.m is invoked:

1. MATDaemon.jl will be installed into a local Julia project, if one does not already exist. By default, a folder .jlcall is created in the same folder as jlcall.m
2. A Julia server will be started in the background using DaemonMode.jl

All subsequent calls to Julia are run on the Julia server. The server will be automatically killed when MATLAB exits.

Restarting the Julia server

In the event that the Julia server reaches an undesired state, the server can be restarted by passing the 'restart' flag with value true:

>> jlcall('', 'restart', true) % restarts the Julia server and returns nothing

Similarly, one can shutdown the Julia server without restarting it:

>> jlcall('', 'shutdown', true) % shuts down the Julia server and returns nothing

Setting up the Julia environment

Before calling Julia functions, it may be necessary or convenient to first set up the Julia environment. For example, one may wish to activate a local project environment, run setup scripts, import modules for later use, or set the number of threads for running multithreaded code.

This setup can be conveniently executed at the start of your MATLAB script with a single call to jlcall.m as follows:

>> jlcall('', ...
    'project', '/path/to/MyProject', ... % activate a local Julia Project
    'setup', '/path/to/setup.jl', ... % run a setup script to load some custom Julia code
    'modules', {'MyProject', 'LinearAlgebra', 'Statistics'}, ... % load a custom module and some modules from Base Julia
    'threads', 'auto', ... % use the default number of Julia threads
    'restart', true ... % start a fresh Julia server environment
    )

See the corresponding sections below for more details about these flags.

Julia multithreading

The number of threads used by the Julia server can be set using the 'threads' flag:

>> jlcall('() -> Threads.nthreads()', 'threads', 8, 'restart', true)

ans =

  int64

   8

The default value for 'threads' is 'auto', deferring to Julia to choose the number of threads.

Note: Julia cannot change the number of threads at runtime. In order for the 'threads' flag to take effect, the server must be restarted.

Loading modules

Julia modules can be loaded and used:

>> jlcall('LinearAlgebra.norm', {[3.0; 4.0]}, 'modules', {'LinearAlgebra'})

ans =

     5

Note: modules are loaded using import, not using. Module symbols must therefore be fully qualified, e.g. LinearAlgebra.norm in the above example as opposed to norm.

Persistent environments

By default, previously loaded Julia code is available on subsequent calls to jlcall.m. For example, following the above call to LinearAlgebra.norm, the LinearAlgebra.det function can be called without loading LinearAlgebra again:

>> jlcall('LinearAlgebra.det', {[1.0 2.0; 3.0 4.0]})

ans =

    -2

Unique environments

Set the 'shared' flag to false in order to evaluate each Julia call in a separate namespace on the Julia server:

% Restart the server, setting 'shared' to false
>> jlcall('LinearAlgebra.norm', {[3.0; 4.0]}, 'modules', {'LinearAlgebra'}, 'restart', true, 'shared', false)

ans =

     5

% This call now errors, despite the above command loading the LinearAlgebra module, as LinearAlgebra.norm is evaluated in a new namespace
>> jlcall('LinearAlgebra.norm', {[3.0; 4.0]}, 'shared', false)
ERROR: LoadError: UndefVarError: LinearAlgebra not defined
Stacktrace:
 ...

Unique Julia instances

Instead of running Julia code on a persistent Julia server, unique Julia instances can be launched for each call to jlcall.m by passing the 'server' flag with value false.

Note: this may cause significant overhead when repeatedly calling jlcall.m due to Julia package precompilation and loading:

>> tic; jlcall('x -> sum(abs2, x)', {1:5}, 'server', false); toc
Elapsed time is 4.181178 seconds. % call unique Julia instance

>> tic; jlcall('x -> sum(abs2, x)', {1:5}, 'restart', true); toc
Elapsed time is 5.046929 seconds. % re-initialize Julia server

>> tic; jlcall('x -> sum(abs2, x)', {1:5}); toc
Elapsed time is 0.267088 seconds. % call server; significantly faster

Loading code from a local project

Code from a local Julia project can be loaded and called:

>> jlcall('MyProject.my_function', args, kwargs, ...
    'project', '/path/to/MyProject', ...
    'modules', {'MyProject'})

Note: the string passed via the 'project' flag is simply forwarded to Pkg.activate; it is the user's responsibility to ensure that the project's dependencies have been installed.

Loading setup code

Julia functions may require or return types which cannot be directly passed from or loaded into MATLAB. For example, suppose one would like to query Base.VERSION. Naively calling jlcall('() -> Base.VERSION') would fail, as typeof(Base.VERSION) is not a String but a VersionNumber.

One possible remedy is to define a wrapper function in a Julia script:

# setup.jl
julia_version() = string(Base.VERSION)

Then, use the 'setup' flag to pass the above script to jlcall.m:

>> jlcall('julia_version', 'setup', '/path/to/setup.jl')

ans =

    '1.6.1'

In this case, jlcall('() -> string(Base.VERSION)') would work just as well. In general, however, interfacing with complex Julia libraries using MATLAB types may be nontrivial, and the 'setup' flag allows for the execution of arbitrary setup code.

Note: the setup script is loaded into the global scope using include; when using persistent environments, symbols defined in the setup script will be available on subsequent calls to jlcall.m.

Handling Julia outputs

Output(s) from Julia are returned using the MATLAB cell array varargout, MATLAB's variable-length list of output arguments. A helper function MATDaemon.matlabify is used to convert Julia values into MATLAB-compatible values. Specifically, the following rules are used to populate varargout with the Julia output y:

1. If y::Nothing, then varargout = {} and no outputs are returned to MATLAB
2. If y::Tuple, then length(y) outputs are returned, with varargout{i} given by matlabify(y[i])
3. Otherwise, one output is returned with varargout{1} given by matlabify(y)

The following matlabify methods are defined by default:

matlabify(x) = x # default fallback
matlabify(::Union{Nothing, Missing}) = zeros(0,0) # equivalent to MATLAB's []
matlabify(x::Symbol) = string(x)
matlabify(xs::Tuple) = Any[matlabify(x) for x in xs] # matlabify values
matlabify(xs::Union{<:AbstractDict, <:NamedTuple, <:Base.Iterators.Pairs}) = Dict{String, Any}(string(k) => matlabify(v) for (k, v) in pairs(xs)) # convert keys to strings and matlabify values

Note: MATLAB cell and struct types correspond to Array{Any} and Dict{String, Any} in Julia.

Conversion via matlabify can easily be extended to additional types. Returning to the example from the above section, we can define a matlabify method for Base.VersionNumber:

# setup.jl
MATDaemon.matlabify(v::Base.VersionNumber) = string(v)

Now, the return type will be automatically converted:

>> jlcall('() -> Base.VERSION', 'setup', '/path/to/setup.jl')

ans =

    '1.6.1'

Performance

MATLAB inputs and Julia ouputs are passed back and forth between MATLAB and the DaemonMode.jl server by writing to temporary .mat files. The location of these files can be configured with the 'infile' and 'outfile' flags, respectively. Pointing these files to a ram-backed file system is recommended when possible (for example, the /tmp folder on Linux is usually ram-backed), as read/write speed will likely improve. This is now the default; 'infile' and 'outfile' are created via the MATLAB tempname function (thanks to @mauro3 for this tip).

Nevertheless, this naturally leads to some overhead when calling Julia, particularly when the MATLAB inputs and/or Julia outputs have large memory footprints. It is therefore not recommended to use jlcall.m in performance critical loops.

MATLAB and Julia version compatibility

This package has been tested on a variety of MATLAB versions. However, for some versions of Julia and MATLAB, supported versions of external libraries may clash. For example, running jlcall.m using Julia v1.6.1 and MATLAB R2015b gives the following error:

>> jlcall

ERROR: Unable to load dependent library ~/.local/julia-1.6.1/bin/../lib/julia/libjulia-internal.so.1

Message: /usr/local/MATLAB/R2015b/sys/os/glnxa64/libstdc++.so.6: version `GLIBCXX_3.4.20' not found (required by ~/.local/julia-1.6.1/bin/../lib/julia/libjulia-internal.so.1)

This error results due to a clash of supported libstdc++ versions, and does not occur when using e.g. Julia v1.5.4 with MATLAB R2015b, or Julia v1.6.1 with MATLAB R2020b.

If you encounter this issue, see the Julia and MATLAB documentation for information on mutually supported external libraries.

About this package

This repository contains utilities for parsing and running Julia code, passing MATLAB arguments to Julia, and retrieving Julia outputs from MATLAB.

The workhorse behind MATDaemon.jl and jlcall.m is DaemonMode.jl which is used to start a persistent Julia server in the background.

Download Details: 
Author: jondeuce
Source Code: https://github.com/jondeuce/MATDaemon.jl 
License: MIT
#julia #matlab