Writing Python in Java syntax is possible with a semi-automatic tool. Programming code translation tools pick up about 75% of dynamically typed language. Conversion of Python to a statically typed language like Java requires some manual translation. The modern Java IDE can be used to infer local variable type definitions for each class attribute and local variable.


Translation of Syntax
Both Python and Java are OO imperative languages with sizable syntax constructs. Python is larger, and more competent for functional programming concepts. Using the source translator tool, parsing of the original Python source language will allow for construction of an Abstract Source Tree (AST), followed by conversion of the AST to Java.

Python will parse itself. This capability is exhibited in the ast module, which includes skeleton classes. The latter can be expanded to parse and source each node of an AST. Extension of the ast.NodeVisitor class enables python syntax constructs to be customized using translate.py and parser.py coding structure.

The Concrete Syntax Tree (CST) for Java is based on visit to the AST. Java string templates can be output at AST nodes with visitor.py code. Comment blocks are not retained by the Python ast Parser. Conversion of Python to multi-line string constructs with the translator reduces time to script.


Scripting Python Type Inference in Java
Programmers using Python source know that the language does not contain type information. The fact that Python is a dynamic type language means object type is determined at run time. Python is also not enforced at compile time, as the source is not specified. Runtime type information of an object can be determined by inspecting the __class__.__name__ attribute.

Python’s inspect module is used for constructing profilers and debugging.
Implementation of def traceit (frame, event, arg) method in Python, and connecting it to the interpreter with sys.settrace (traceit) allows for integration of multiple events during application runtime.

Method call events prompt inspect and indexing of runtime type. Inspection of all method arguments can be conducted. By running the application profiler and exercising the code, captured trace files for each source file can be modified with the translator. Generating method syntax can be done with the translator by search and addition of type information. Results in set or returned variables disseminate the dynamic code in static taxonomy.

The final step in the Python to Java scrip integration is to administer unsupported concepts such as value object creation. There is also the task of porting library client code, for reproduction in Java equivalents. Java API stubs can be created to account for Python APIs. Once converted to Java the final clean-up of the script is far easier.

Related:

 What Are The 10 Most Famous Software Programs Written in Python?

Python, a Zen Poem

Much of success is about performance. It’s about what we do and what we are able to inspire others to do. There are some simple performance principles I have learned in my life, and I want to share them with you.  They really bring success, and what it takes to be successful, into sharp focus. They are also the basis for developing and maintaining an expectation of success.

The Five Principles of Performance

1. We generally get from ourselves and others what we expect. It is a huge fact that you will either live up or down to your own expectations. If you expect to lose, you will. If you expect to be average, you will be average. If you expect to feel bad, you probably will. If you expect to feel great, nothing will slow you down. And what is true for you is true for others. Your expectations for others will become what they deliver and achieve. As Gandhi said, “Be the change you wish to see in the world.”

2. The difference between good and excellent companies is training. The only thing worse than training employees and losing them is to not train them and keep them! A football team would not be very successful if they did not train, practice, and prepare for their opponents. When you think of training as practice and preparation, it makes you wonder how businesses survive that do not make significant training investments in their people.

Actually, companies that do not train their people and invest in their ability don’t last. They operate from a competitive disadvantage and are eventually gobbled up and defeated in the marketplace. If you want to improve and move from good to excellent, a good training strategy will be the key to success.

Studying a functional programming language is a good way to discover new approaches to problems and different ways of thinking. Although functional programming has much in common with logic and imperative programming, it uses unique abstractions and a different toolset for solving problems. Likewise, many current mainstream languages are beginning to pick up and integrate various techniques and features from functional programming.

Many authorities feel that Haskell is a great introductory language for learning functional programming. However, there are various other possibilities, including Scheme, F#, Scala, Clojure, Erlang and others.

Haskell is widely recognized as a beautiful, concise and high-performing programming language. It is statically typed and supports various cool features that augment language expressivity, including currying and pattern matching. In addition to monads, the language support a type-class system based on methods; this enables higher encapsulation and abstraction. Advanced Haskell will require learning about combinators, lambda calculus and category theory. Haskell allows programmers to create extremely elegant solutions.

Scheme is another good learning language -- it has an extensive history in academia and a vast body of instructional documents. Based on the oldest functional language -- Lisp -- Scheme is actually very small and elegant. Studying Scheme will allow the programmer to master iteration and recursion, lambda functions and first-class functions, closures, and bottom-up design.

Supported by Microsoft and growing in popularity, F# is a multi-paradigm, functional-first programming language that derives from ML and incorporates features from numerous languages, including OCaml, Scala, Haskell and Erlang. F# is described as a functional language that also supports object-oriented and imperative techniques. It is a .NET family member. F# allows the programmer to create succinct, type-safe, expressive and efficient solutions. It excels at parallel I/O and parallel CPU programming, data-oriented programming, and algorithmic development.

Scala is a general-purpose programming and scripting language that is both functional and object-oriented. It has strong static types and supports numerous functional language techniques such as pattern matching, lazy evaluation, currying, algebraic types, immutability and tail recursion. Scala -- from "scalable language" -- enables coders to write extremely concise source code. The code is compiled into Java bytecode and executes on the ubiquitous JVM (Java virtual machine).

Like Scala, Clojure also runs on the Java virtual machine. Because it is based on Lisp, it treats code like data and supports macros. Clojure's immutability features and time-progression constructs enable the creation of robust multithreaded programs.

Erlang is a highly concurrent language and runtime. Initially created by Ericsson to enable real-time, fault-tolerant, distributed applications, Erlang code can be altered without halting the system. The language has a functional subset with single assignment, dynamic typing, and eager evaluation. Erlang has powerful explicit support for concurrent processes.

Computer Programming as a Career?

What little habits make you a better software engineer?

Facebook has recently released a collection of C++ software modules that it uses to run the popular website.  With Facebook releasing Folly (the name it designated for the collection), more of the internal programs could become open source since they need different parts of the collection.

Jordan DeLong, a Facebook software engineer, said one concerning holdup to releasing additional work is that any open source project had to cut away from the dependencies on non-released internal collection code.