Working with 1D arrays from Python package in C++

This article provides an introduction to cross-technology handling of one-dimensional arrays. Data structures are one of the essential aspects of every piece of software. Any application constantly process various information, that very often require specific grouping and access strategies. This aspect is addressed by arrays and more advanced collection types. By using the Javonet framework, users gain ability to easily and effectively work with data structures originating from Python package. Every array from Python package is treated as reference.

Javonet allows you to reference and use modules or packages written in (Java/Kotlin/Groovy/Clojure, C#/VB.NET, Ruby, Perl, Python, JavaScript/TypeScript) like they were created in your technology. If have not yet created your first project check Javonet overview and quick start guides for your technology.

With Javonet you can interact with arrays from Python package like they were available in C++ but invocation must be performed through Javonet SDK API.

Custom Python package with arrays handling

With Javonet it is possible to reference any custom Python package and interact with arrays declared on types defined within that module almost the same as with any other C++ library.

Snippet below represents the sample code from Python package that has methods which return or process the arrays:

def get_1d_array(self):
	return ["one", "two", "three", "four", "five"]

def get_2d_array(self):
	return [["S00", "S01"], ["S10","S11"]]

def add_array_elements_and_multiply(self, my_array, my_value):
	return sum(my_array) * my_value

Javonet SDK contains various methods to interact with arrays and consume the results in C++:

Get element of an 1D array from Python package in C++

// use Activate only once in your app
Javonet::Activate("your-license-key");

// create called runtime context
auto calledRuntime = Javonet::InMemory()->Python();

// set up variables
auto libraryPath = resourcesDirectory;
auto className = "TestClass.TestClass";

// load custom library
calledRuntime->LoadLibrary(libraryPath);

// get type from the runtime
auto calledRuntimeType = calledRuntime->GetType(className)->Execute();

// create type's instance
auto instance = calledRuntimeType->CreateInstance({ 0,1 })->Execute();

// invoke instance's method
auto arrayReference = instance->InvokeInstanceMethod("get_1d_array")->Execute();

// get index from array
auto response = arrayReference->GetIndex(2)->Execute();

// get value from response
auto result = std::any_cast<std::string>(response->GetValue());

// write result to console
std::cout << result << std::endl;

In the snippet above, get1DArray method is used to get reference to 1D array from Python package. Method getIndex is used to get element from the array.

Set element of an 1D array from Python package in C++

// use Activate only once in your app
Javonet::Activate("your-license-key");

// create called runtime context
auto calledRuntime = Javonet::InMemory()->Python();

// set up variables
auto libraryPath = resourcesDirectory;
auto className = "TestClass.TestClass";

// load custom library
calledRuntime->LoadLibrary(libraryPath);

// get type from the runtime
auto calledRuntimeType = calledRuntime->GetType(className)->Execute();

// create type's instance
auto instance = calledRuntimeType->CreateInstance({ 0,1 })->Execute();

// invoke instance's method
auto arrayReference = instance->InvokeInstanceMethod("get_1d_array")->Execute();

// set array's index
arrayReference->SetIndex(4, "seven")->Execute();

// get index from array
auto response = arrayReference->GetIndex(4)->Execute();

// get value from response
auto result = std::any_cast<std::string>(response->GetValue());

// write result to console
std::cout << result << std::endl;

In the snippet above, get1DArray method is used to get reference to 1D array from Python package. Method setIndex is used to set element of the 1D array. First argument is index, second argument is new value.

Get size of an array from Python package in C++

// use Activate only once in your app
Javonet::Activate("your-license-key");

// create called runtime context
auto calledRuntime = Javonet::InMemory()->Python();

// set up variables
auto libraryPath = resourcesDirectory;
auto className = "TestClass.TestClass";

// load custom library
calledRuntime->LoadLibrary(libraryPath);

// get type from the runtime
auto calledRuntimeType = calledRuntime->GetType(className)->Execute();

// create type's instance
auto instance = calledRuntimeType->CreateInstance({ 0,1 })->Execute();

// invoke instance's method
auto arrayReference = instance->InvokeInstanceMethod("get_1d_array")->Execute();

// get array's size
auto response = arrayReference->GetSize()->Execute();

// get value from response
auto result = std::any_cast<int>(response->GetValue());

// write result to console
std::cout << result << std::endl;

In the snippet above, get1DArray method is used to get reference to 1D array from Python package. Method getSize is used to get number of elements of the array.

The same operation can be performed remotely by just changing the new Runtime Context invocation from in memory to tcp that will create and interact with your Python package objects on any remote node, container or service that hosts Javonet Code Gateway. This way you can preserve the same logic in your application and instantly switch between monolithic and microservices architecture without the need to implement the integration layer based on web services or other remote invocation methods.

Read more about use cases and software architecture scenarios where Javonet runtime bridging technology can support your development process.