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

Outsourcing IT needs in the corporate world has become extremely popular because it is cost-efficient and it gives IT resources to companies that may not otherwise be able to afford them. Another positive side effect of IT outsourcing is that it has brought many technology jobs back to the United States.

As convenient and patriotic as IT outsourcing has become, it also have several limitations that have caused many companies to re-think the idea of funding their own internal IT group. It is important for a company to be completely familiar with these limitations before developing any kind of company policy in regards to IT.

The Customer Could Outgrow The Outsource Company

When an IT outsourcing company first takes on a new client, the relationship is beneficial to both sides. But things can start to get inconvenient for the client when the client's business starts to outgrow the capabilities of the IT outsource group.

An IT outsourcing company can become entrenched in the daily routine of its clients, which is great at first. But when the IT company can no longer keep pace with the growth of its clients, then the clients are stuck trying to find a new solution and keep track of its own IT assets at the same time.

The IT Outsourcing Company Lacks The Necessary Technical Expertise

IT outsourcing clients like to believe that their support company knows everything there is to know about computers and the Internet. But every IT support company has its areas of expertise and they have the technical areas where they are not quite as strong. If the client starts to experience needs from the areas where the IT outsourcing company is not so strong, then that can become a significant business issue.

This problem can be amplified if the client is a small business experiencing growth. An IT outsourcing company is not as likely to bring on new personnel for a smaller client, which leaves the client without a solution.

The Client Losses A Measure Of Control Over Its Data

No matter how large or successful an IT outsourcing company may be, there will always be the limitation of client security and the protection of critical customer data. All it takes is one rogue employee of the IT outsourcing company to compromise all of the client's critical data.

Some IT outsourcing companies have safeguards put in place to try and prevent client data compromise, but those safeguards are limited by how much access the IT company has to the client network. In most cases, that access has to be comprehensive for the IT outsourcing company to be able to do its job.

Outsourcing IT responsibilities can take a lot of stress off of a client and allow that client to operate his business by focusing on his core competencies. But there are limitations to IT outsourcing that could make it necessary for a client to do his own IT support and pay the extra costs.

Let’s face it, fad or not, companies are starting to ask themselves how they could possibly use machine learning and AI technologies in their organization. Many are being lured by the promise of profits by discovering winning patterns with algorithms that will enable solid predictions… The reality is that most technology and business professionals do not have sufficient understanding of how machine learning works and where it can be applied.  For a lot of firms, the focus still tends to be on small-scale changes instead of focusing on what really matters…tackling their approach to machine learning.

In the recent Wall Street Journal article, Machine Learning at Scale Remains Elusive for Many Firms, Steven Norton captures interesting comments from the industry’s data science experts. In the article, he quotes panelists from the MIT Digital Economy Conference in NYC, on businesses current practices with AI and machine learning. All agree on the fact that, for all the talk of Machine Learning and AI’s potential in the enterprise, many firms aren’t yet equipped to take advantage of it fully.

Panelist,  Michael Chui, partner at McKinsey Global Institute states that “If a company just mechanically says OK, I’ll automate this little activity here and this little activity there, rather than re-thinking the entire process and how it can be enabled by technology, they usually get very little value out of it. “Few companies have deployed these technologies in a core business process or at scale.”

Panelist, Hilary Mason, general manager at Cloudera Inc., had this to say, “With very few exceptions, every company we work with wants to start with a cost-savings application of automation.” “Most organizations are not set up to do this well.”

Creating an enum in Python prior to Python 3.4 was accomplished as follows:

def enum(**enums)::
      return type('Enum',(),enums)

then use as:

Animals=enum(Dog=1,Cat=2)

and accessed as:

Animals.Dog

The new version can be created as follows:

from enum import Enum

class Animal(Enum):
    Dog=1
    Cat=2