jnumpy: Writing Python C Extensions With Julia

JNumPy: writing high-performance C extensions for Python in minutes

Install JNumPy

Requirements:

• Python >= 3.7

You can install the Python package jnumpy with the following command:

pip install julia-numpy.

Note that JNumPy will install julia in JNUMPY_HOME for you, if there is no Julia installation available.

Usage

• create a Python package example, write and export julia functions in the file example/src/example.jl

module example

using TyPython
using TyPython.CPython

@export_py function mat_mul(a::AbstractArray, b::AbstractArray)::Array
    return a * b
end

function init()
    @export_pymodule _example begin
        jl_mat_mul = Pyfunc(mat_mul)
    end
end

# the following code is optional,
# but makes Python code loading much faster since the second time.
precompile(init, ())

end

• create example/Project.toml as follows:

name = "example"  # this is required to find the julia's entry module

[deps]
# specify your julia dependencies here

• initialize and import the julia functions at example/__init__.py:

import jnumpy as np
# you may call np.set_julia_mirror(server) to set the julia package server,
# or leave the argument server empty to automatically search the nearest mirror.
np.init_jl()
np.init_project(__file__)

from _example import jl_mat_mul

__all__ = ['jl_mat_mul']

• enjoy your Python extension package:

This is the structure of your Python extension package:

> ls -R
example/:
    __init__.py  Project.toml  src

example/src:
    example.jl

• This is how you use it:

from example import jl_mat_mul
x = np.array([[1,2],[3,4]])
y = np.array([[4,3],[2,1]])
jl_mat_mul(x, y)
# array([[ 8,  5],
#       [20, 13]])

Environment Variables

JNUMPY_HOME:

The home directory for JNumPy-specific settings. The default value is ~/.jnumpy. JNumPy runs julia in a default environment ($JNUMPY_HOME/envs/default). In case that you don't have a julia executable, JNumPy installs julia into $JNUMPY_HOME using jill.py.

TYPY_JL_EXE:

The path of the julia executable in use.

TYPY_JL_OPTS:

Command-line options when launching julia. If you want to use a custom environment, you could set --project=<dir>. TYPY_JL_OPTS is the same as those arguments passed to julia.

Examples

There are several examples presented in the demo directory. Those examples are standalone Python packages created using JNumPy, and can be imported if you have JNumPy installed.

demo/basic: a tiny Python package to give an example of how to use JNumPy.

demo/kmeans: a tiny Python package wrapping ParallelKMeans.jl. It produces a 10x performance gain against Scikit-Learn.

demo/fft: a tiny Python package wrapping FFTW.jl, and indirectly the GPL-licenced FFTW library. It allows users to access FFT plans for accelerating FFTs.

Download Details: 
Author: Suzhou-Tongyuan
Source Code: https://github.com/Suzhou-Tongyuan/jnumpy 
License: MIT
#python #julia 
 

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Buddha Community

Lambda, Map, Filter functions in python

Welcome to my Blog, In this article, we will learn python lambda function, Map function, and filter function.

Lambda function in python: Lambda is a one line anonymous function and lambda takes any number of arguments but can only have one expression and python lambda syntax is

Syntax: x = lambda arguments : expression

Now i will show you some python lambda function examples:

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Why use Python for Software Development

No programming language is pretty much as diverse as Python. It enables building cutting edge applications effortlessly. Developers are as yet investigating the full capability of end-to-end Python development services in various areas. 

By areas, we mean FinTech, HealthTech, InsureTech, Cybersecurity, and that's just the beginning. These are New Economy areas, and Python has the ability to serve every one of them. The vast majority of them require massive computational abilities. Python's code is dynamic and powerful - equipped for taking care of the heavy traffic and substantial algorithmic capacities. 

Programming advancement is multidimensional today. Endeavor programming requires an intelligent application with AI and ML capacities. Shopper based applications require information examination to convey a superior client experience. Netflix, Trello, and Amazon are genuine instances of such applications. Python assists with building them effortlessly. 

5 Reasons to Utilize Python for Programming Web Apps 

Python can do such numerous things that developers can't discover enough reasons to admire it. Python application development isn't restricted to web and enterprise applications. It is exceptionally adaptable and superb for a wide range of uses.

Robust frameworks 

Python is known for its tools and frameworks. There's a structure for everything. Django is helpful for building web applications, venture applications, logical applications, and mathematical processing. Flask is another web improvement framework with no conditions. 

Web2Py, CherryPy, and Falcon offer incredible capabilities to customize Python development services. A large portion of them are open-source frameworks that allow quick turn of events. 

Simple to read and compose 

Python has an improved sentence structure - one that is like the English language. New engineers for Python can undoubtedly understand where they stand in the development process. The simplicity of composing allows quick application building. 

The motivation behind building Python, as said by its maker Guido Van Rossum, was to empower even beginner engineers to comprehend the programming language. The simple coding likewise permits developers to roll out speedy improvements without getting confused by pointless subtleties. 

Utilized by the best 

Alright - Python isn't simply one more programming language. It should have something, which is the reason the business giants use it. Furthermore, that too for different purposes. Developers at Google use Python to assemble framework organization systems, parallel information pusher, code audit, testing and QA, and substantially more. Netflix utilizes Python web development services for its recommendation algorithm and media player. 

Massive community support 

Python has a steadily developing community that offers enormous help. From amateurs to specialists, there's everybody. There are a lot of instructional exercises, documentation, and guides accessible for Python web development solutions. 

Today, numerous universities start with Python, adding to the quantity of individuals in the community. Frequently, Python designers team up on various tasks and help each other with algorithmic, utilitarian, and application critical thinking. 

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Python is the greatest supporter of data science, Machine Learning, and Artificial Intelligence at any enterprise software development company. Its utilization cases in cutting edge applications are the most compelling motivation for its prosperity. Python is the second most well known tool after R for data analytics.

The simplicity of getting sorted out, overseeing, and visualizing information through unique libraries makes it ideal for data based applications. TensorFlow for neural networks and OpenCV for computer vision are two of Python's most well known use cases for Machine learning applications.

Summary

Thinking about the advances in programming and innovation, Python is a YES for an assorted scope of utilizations. Game development, web application development services, GUI advancement, ML and AI improvement, Enterprise and customer applications - every one of them uses Python to its full potential. 

The disadvantages of Python web improvement arrangements are regularly disregarded by developers and organizations because of the advantages it gives. They focus on quality over speed and performance over blunders. That is the reason it's a good idea to utilize Python for building the applications of the future.

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Python Tricks Every Developer Should Know

Python is awesome, it’s one of the easiest languages with simple and intuitive syntax but wait, have you ever thought that there might ways to write your python code simpler?

In this tutorial, you’re going to learn a variety of Python tricks that you can use to write your Python code in a more readable and efficient way like a pro.

Let’s get started

Swapping value in Python

Instead of creating a temporary variable to hold the value of the one while swapping, you can do this instead

>>> FirstName = "kalebu"
>>> LastName = "Jordan"
>>> FirstName, LastName = LastName, FirstName 
>>> print(FirstName, LastName)
('Jordan', 'kalebu')

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How to Remove all Duplicate Files on your Drive via Python

Today you’re going to learn how to use Python programming in a way that can ultimately save a lot of space on your drive by removing all the duplicates.

Intro

In many situations you may find yourself having duplicates files on your disk and but when it comes to tracking and checking them manually it can tedious.

Heres a solution

Instead of tracking throughout your disk to see if there is a duplicate, you can automate the process using coding, by writing a program to recursively track through the disk and remove all the found duplicates and that’s what this article is about.

But How do we do it?

If we were to read the whole file and then compare it to the rest of the files recursively through the given directory it will take a very long time, then how do we do it?

The answer is hashing, with hashing can generate a given string of letters and numbers which act as the identity of a given file and if we find any other file with the same identity we gonna delete it.

There’s a variety of hashing algorithms out there such as

• md5
• sha1
• sha224, sha256, sha384 and sha512

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Python vs C++: Difference Between Python and C++ [2021]

There has been a great deal of discussion surrounding Python and C++ as to which is the better learning tool in the programming paradigm. However, there is no right answer to that. Python is more suitable for web programming while C++ scores where hardware-related programming is concerned. In any case, both languages differ from each other in a number of ways and have varied uses.

In this article, we will look at the features and applications of both programming languages and draw a comparison between the two. So, let’s get started!

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