#modules in python with examples

[yoneda-emma]

This is sort of an indirect addition to this ask maddy got about learning rust, I haven't actually gotten to learning rust yet but I do want to talk about an incident I recently had with python (I think with C the reasons why you'd rather not use it are fairly obvious) So recently tried to implement a basic matrix class in python, and since I wanted to do a whole thing where I did my own implementations of numerical optimization and more general linear algebra stuff, I tried to do it "properly" and make it "robust" and everything, but without installing any external libraries.

And to me, that obviously involved making sure that the matrix is formatted sensibly, i.e. that a matrix contains m rows of n numbers each. This seemed like a very obvious thing you should do in any serious piece of code, since if the contents of a matrix are accidentally formatted in a weird way, then you might get errors, or, significantly worse, python might just decide that it "can handle them" anyways and do some really unintuitive dumb stuff that's really hard to debug. (See this older post of mine for an example of how the pythonic willingness to work with bad inputs leads to really weird unintuitive behavior).

Turns out this is not something you can do directly in python without installing external type checking libraries! And I didn't want to just loop through all the contents and check their type individually during object creation, since that felt incredibly slow, stupid and inefficient. It didnt help that my algorithms theory exam was coming up soon, which meant I was thinking about asymptotic runtimes all day.

And so I was like "well surely at least it's easy to check for a matrix being a 2D array with consistent row sizes". However, at this point, with dawning horror, I came to a realization:

and at this moment I could just feel pretty much all of my remaining "python is easy to work with" attitude turning into dust and soaring away in the wind. If anyone here knows a way to enforce a given argument being a 2D array of numbers with consistent row sizes that doesn't involve O(n*m) overhead during object creation and that can be implemented in python using only internal modules (no external type checkers that need to be installed manually first) please tell me lol

[a-fox-studies]

hi! i'm shy as hell usually but trying to branch out cuz i saw u reblog that post abt asks. do u have any recs on where to start for a beginner coder (who has virtually no free time)? i feel like the landscape is so vast and overwhelming.

Hiii!! Thanks for the ask!

I think the best thing I've learned throughout my coding journey is that you must first pick a field that interests you. Computer Science is a VAST world, once you pick a field, you must really commit to it. For example, maybe data science interests you. You look up the requirements to be a date scientist. I don't have in-depth knowledge on it, but I do know that you need good knowledge about python and some of its modules like numpy, matplotlib etc. So you start learning the basics, and then move on to the modules. The key is to find what you love, then find what you need in order to pursue it.

For a kickstart I'd recommend w3schools — it's a really good site that has so many tutorials on various languages.

I was also a person that had no free time at all to focus on my learning. But then I realised that you must make time if you want to work on something with dedication. So now I devote Sunday afternoons to learning and working on my projects, and I try to stick to it as much as possible, except for when I have uni exams or any submissions lol

Hope this helped!!

[andyet-i-fish]

my first time using Python's turtle module‼️(basically you can code fairly geometric designs using a "turtle" as a pencil and it compiles line by line as you run it.) the inspiration was a (heavily simplified) Łowicz folkdress.

^video of it compiling in real time

below you can see the deranged planning I did + some of the final code ⌄

the program was 498 lines long so here's just some of the more interesting stuff I wrote 💀

my strategy at first was to put in all coordinates by hand, and i completed the whole design that way; but then my prof said he wanted us to use more functions and not just repeat anything unnecessarily so i rewrote any repeating shapes (the skirt stripes for one example) to be functions where the new x and y coordinates would be manipulated by parameters telling what iteration the stripe was, what side it was on, etc. it looks utterly incomprehensible but it works!

[vintagerpg]

They can’t all be winners. This is The Phantastical Phantasmagorical Montie Haul Dungeon (1982). The front cover claims not Gamelords as the publisher, but No-Shamelords Unlimited. That gives you an excellent sense of the humor that you will encounter inside.

Like Compleat Tavern, this is a zine-sized book. It presents [deep breath] the Pyramid of Pallapot the Peripatetic, a senile old mage, and is, basically, one big goof in the style of, I dunno, say, those April Fool’s issues of Dragon Magazine I roll my eyes at so hard. I don’t really understand the compulsion RPG folks have for producing stuff like this occasionally, but honestly, I also don’t understand why they almost always leave me cold. RPGs can certainly be funny — Paranoia, Honey Heist and more do it intentionally, and I don’t know a single game I’ve ever run that hasn’t had at least one moment of sidesplitting laughter. But it seems like typing up these sorts of one-of joke modules drains any humor that might have been there out of the proceedings. They remind me of paperback books you’d use to see that would collect one-liner jokes. They try too hard to nail the punchline when really they should just be concentrating on setting up the gags for the players to riff on. Dismaying.

You might doubt me. Here, let me give you an example. One room riffs on Monty Python and the Holy Grail, because of course it does. There’s a black knight you can defeat by hacking to pieces. The twist is that he’s the film’s knight’s younger brother, who is cursed to do everything backwards, like Bizarro, sort of. His actual name is “Knight Black The.” God, that hurt to type out.

Yea, no, I can’t go on.

[optimisticduckbird]

Today I learned that if you write something between two sets of three quotation marks in python, it's like doing a comment, kind of, and it's called a docstring. You use it to document your code. In the reST format, you use colons to describe parameters included in your code. On Stackoverflow.com, someone called daouzli put this example:

"""

This is a reST style.

:param param1: this is a first param

:param param2:

this is a second param

:returns: this is a description of what is returned

:raises keyError: raises an exception

"""

I had to look it up because my module in python coding was dropping it into all the examples without any context or explanation!

I also learned that you use def to declare a user-defined function which in plain talk would be a function that you've made and you use def to give it a name so you can call it up easily. For example, if you knew you might want to print a specific combination of things multiple times, you might make a function for it and call the function instead of writing out the whole combination every time.

[digitalmarketing1225]

Object-Oriented Programming (OOP) Explaine

Object-Oriented Programming (OOP) is a programming paradigm based on the concept of "objects," which represent real-world entities. Objects combine data (attributes) and functions (methods) into a single unit. OOP promotes code reusability, modularity, and scalability, making it a popular approach in modern software development.

Core Concepts of Object-Oriented Programming

Classes and Objects

Class: A blueprint or template for creating objects. It defines properties (attributes) and behaviors (methods).

Object: An instance of a class. Each object has unique data but follows the structure defined by its

Encapsulations

Encapsulation means bundling data (attributes) and methods that operate on that data within a class. It protects object properties by restricting direct access.

Access to attributes is controlled through getter and setter methods.Example: pythonCopyEditclass Person: def __init__(self, name): self.__name = name # Private attribute def get_name(self): return self.__name person = Person("Alice") print(person.get_name()) # Output: Alice

Inheritance

Inheritance allows a class (child) to inherit properties and methods from another class (parent). It promotes code reuse and hierarchical relationships.Example: pythonCopyEditclass Animal: def speak(self): print("Animal speaks") class Dog(Animal): def speak(self): print("Dog barks") dog = Dog() dog.speak() # Output: Dog barks

Polymorphism

Polymorphism allows methods to have multiple forms. It enables the same function to work with different object types.

Two common types:

Method Overriding (child class redefines parent method).

Method Overloading (same method name, different parameters – not natively supported in Python).Example: pythonCopyEditclass Bird: def sound(self): print("Bird chirps") class Cat: def sound(self): print("Cat meows") def make_sound(animal): animal.sound() make_sound(Bird()) # Output: Bird chirps make_sound(Cat()) # Output: Cat meows

Abstraction

Abstraction hides complex implementation details and shows only the essential features.

In Python, this is achieved using abstract classes and methods (via the abc module).Example: pythonCopyEditfrom abc import ABC, abstractmethod class Shape(ABC): @abstractmethod def area(self): pass class Circle(Shape): def __init__(self, radius): self.radius = radius def area(self): return 3.14 * self.radius * self.radius circle = Circle(5) print(circle.area()) # Output: 78.5

Advantages of Object-Oriented Programming

Code Reusability: Use inheritance to reduce code duplication.

Modularity: Organize code into separate classes, improving readability and maintenance.

Scalability: Easily extend and modify programs as they grow.

Data Security: Protect sensitive data using encapsulation.

Flexibility: Use polymorphism for adaptable and reusable methods.

Real-World Applications of OOP

Software Development: Used in large-scale applications like operating systems, web frameworks, and databases.

Game Development: Objects represent game entities like characters and environments.

Banking Systems: Manage customer accounts, transactions, and security.

E-commerce Platforms: Handle products, users, and payment processing.

Machine Learning: Implement models as objects for efficient training and prediction.

Conclusion

Object-Oriented Programming is a powerful paradigm that enhances software design by using objects, encapsulation, inheritance, polymorphism, and abstraction. It is widely used in various industries to build scalable, maintainable, and efficient applications. Understanding and applying OOP principles is essential for modern software development.

: pythonCopyEdit

class Car: def __init__(self, brand, model): self.brand = brand self.model = model def display_info(self): print(f"Car: {self.brand} {self.model}") my_car = Car("Toyota", "Camry") my_car.display_info() # Output: Car: Toyota Camry

Encapsulation

[angstics]

how to download (and mirror and transcribe) youtube videos

so the news that google is deleting inactive youtube channels was a miscommunication -- "Additionally, we do not have plans to delete accounts with YouTube videos at this time" (source, emphasis mine). but i hope this was a wake up call that archiving videos (and other content) you care about is really important. buy hard drives, save, reshare. videos dont stay up forever. youtube isnt forever.

i know how difficult it is to get into downloading videos, with how all youtube to mp4 websites seem to be broken. this post compiles general guides on how to manually download youtube videos (among other actions) through python programs. it's simple if you just follow the steps and constantly search the errors you encounter. i will also detail how i personally do it with my windows 10 pc, in case you use the same tools.

remember: your search engine, reddit, github, and help commands are your best friends.

* downloading youtube videos

reddit yt-dlp guide

original yt-dlp guide

how to download the best quality mp4

how to download videos from a search result

how to use command prompt

what is command prompt? this is a windows application where you navigate folders and run programs. you just type a command and hit enter. ctrl+c ends a command/program, ctrl+s pauses it (pressing any key unpauses)

how do i navigate folders? the basic commands are so: a) cd "[path]" to change directory (always put path and link names in double quotes so they are processed properly), b) cd .\.. takes you to the previous folder (ex: if you're in C:\folder A\folder B and run cd .\.. you go to C:\folder A), c) you can go to other drives by typing the letter and colon (ex: if you are in C:, typing D: then entering takes you to your D drive). this is important because where your python programs are stored is where you have to run them.

how to run python programs through cmd prompt? a) download the latest version of python. b) use pip to install programs. c) make sure you have also downloaded a program's dependencies (analogous to "pre-requisites"). d) type the program name then the command.

make sure to always update python and pip.

how to use yt-dlp to download youtube videos

how to get download yt-dlp? this guide worked perfectly for me. make sure to download all python programs in the same folder.

navigate to the folder you installed yt-dlp

the following are examples of commands you can use:

yt-dlp -h -- get a list of all commands

yt-dlp "[link]" -- download video as is (often in webm format)

yt-dlp "[link]" -f "bestvideo[ext=mp4]+bestaudio[ext=m4a]/best[ext=mp4]/best" -- download the highest quality mp4 video (highest possible in mp4 is 1080p)

yt-dlp -x --audio-format mp3 "[link]" -- download audio only as mp3

yt-dlp -i "[playlist link]" -- download a full playlist (you may also use the best quality command here)

yt-dlp -i "[playlist link]" --playlist-items [range] -- download range of playlist items

look at the guides at the top of this section for my ideas of what you can do with yt-dlp. you can even use yt-dlp to download from other websites

note: if you want to download instagram reels, you must include: --cookies-from-browser [firefox / chrome / etc] -- choose your browser

** mirroring youtube videos to archive.org

github tubeup guide: "tubeup uses yt-dlp to download a Youtube video (or any other provider supported by yt-dlp), and then uploads it with all metadata to the Internet Archive using the python module internetarchive."

this guide shows you how to install and use the program. this is an easy way to archive videos with the proper metadata -- do not archive videos en masse

the mirrortube archive.org community

*** transcribing videos

transcribing youtube videos w/o downloading: application

transcribing any downloaded video: openai guide

extra1: searching videos

ive seen confusion on how to naviagte youtube search these days. i know!!!! here are some tips:

changing search options to search by upload date shows *ACTUAL* results, rather than suggestions.

the same google tricks work on youtube: google tricks guide

using yt-dlp to search can be helpful to search youtube more precisely

extra2: downloading twitter videos online

i use this regularly, so i thought id also share.

[pandeypankaj]

How do I learn Python in depth?

Improving Your Python Skills

  Writing Python Programs Basics: Practice the basics solidly. 

  Syntax and Semantics: Make sure you are very strong in variables, data types, control flow, functions, and object-oriented programming. 

 Data Structures: Be able to work with lists, tuples, dictionaries, and sets, and know when to use which. 

 Modules and Packages: Study how to import and use built-in and third-party modules. 

Advanced Concepts

Generators and Iterators: Know how to develop efficient iterators and generators for memory-efficient code. 

Decorators: Learn how to dynamically alter functions using decorators. 

Metaclasses: Understand how classes are created and can be customized. 

Context Managers: Understand how contexts work with statements. 

Project Practice 

 Personal Projects: You will work on projects that you want to, whether building a web application, data analysis tool, or a game.

 Contributing to Open Source: Contribute to open-source projects in order to learn from senior developers. Get exposed to real-life code. 

 Online Challenges: Take part in coding challenges on HackerRank, LeetCode, or Project Euler. 

 Learn Various Libraries and Frameworks

 Scientific Computing: NumPy, SciPy, Pandas

 Data Visualization: Matplotlib, Seaborn

 Machine Learning: Scikit-learn, TensorFlow, PyTorch

 Web Development: Django, Flask

Data Analysis: Dask, Airflow

Read Pythonic Code

 Open Source Projects: Study the source code of a few popular Python projects. Go through their best practices and idiomatic Python. 

 Books and Tutorials: Read all the code examples in books and tutorials on Python. 

 Conferences and Workshops

  Attend conferences and workshops that will help you further your skills in Python. PyCon is an annual Python conference that includes talks, workshops, and even networking opportunities. Local meetups will let you connect with other Python developers in your area. 

Learn Continuously

 Follow Blogs and Podcasts: Keep reading blogs and listening to podcasts that will keep you updated with the latest trends and developments taking place within the Python community.

Online Courses: Advanced understanding in Python can be acquired by taking online courses on the subject.

 Try It Yourself: Trying new techniques and libraries expands one's knowledge.

Other Recommendations

 Readable-Clean Code: For code writing, it's essential to follow the style guide in Python, PEP 

Naming your variables and functions as close to their utilization as possible is also recommended.

 Test Your Code: Unit tests will help in establishing the correctness of your code.

 Coding with Others: Doing pair programming and code reviews would provide you with experience from other coders.

 You are not Afraid to Ask for Help: Never hesitate to ask for help when things are beyond your hand-on areas, be it online communities or mentors.

These steps, along with consistent practice, will help you become proficient in Python development and open a wide range of possibilities in your career.

[mr-jython]

Introduction to Python

Python is a widely used general-purpose, high level programming language. It was initially designed by Guido van Rossum in 1991 and developed by Python Software Foundation. It was mainly developed for emphasis on code readability, and its syntax (set of rules that govern the structure of a code) allows programmers to express concepts in fewer lines of code.

Python is a programming language that lets you work quickly and integrate systems more efficiently.

data types: Int(integer), float(decimal), Boolean(True or False), string, and list; variables, expressions, statements, precedence of operators, comments; modules, functions-- - function and its use, flow of execution, parameters and arguments.

Programming in python

To start programming in Python, you will need an interpreter. An interpreter is basically a software that reads, translates and executes the code line by line instead of combining the entire code into machine code as a compiler does.

Popular interpreters in python

Cpython

Jython

PyPy

IronPython

MicroPython

IDEs

Many other programmers also use IDEs(Integrated Development Environment) which are softwares that provide an extensive set of tools and features to support software development.

Examples of IDEs

Pycharm

Visual studio code (VS code)

Eclipse

Xcode

Android studio

Net beans

[mlembug]

Relay operated power button

What you need:

a 5V relay module

Raspberry Pi

a bunch of cables

Explanation:

A power button on a computer case lets electricity flow between two power pins upon it being pressed, which is when the motherboard detects the button is pressed and reacts accordingly.

A relay is a specific kind of switch which lets electricity flow conditionally upon the flow of electricity in another circuit. The voltages involved both in motherboard power pins and on Raspberry Pi are generally low to not damage both, but we're taking extra precautions to electrically separate them both.

"5V relay module" here means that 5V is the voltage that is required for the relay module to work, while the controlling voltage can be lower. It being relay module it means it also has a flyback diode we'd otherwise have to provide ourselves.

Raspberry Pi's GPIO pins are programmable and can be controlled through Python code, and operate on 3.3V. Raspberry Pi also provides 5V output, but this one is not controllable.

By

connecting a power button to rPi GPIO pins

connecting the 5V voltage output pin from rPi to the relay module's Vcc input pin

connecting the ground pin from rPi to the relay module's groud pin

connecting programmable GPIO pins as the relay module's input pin

connecting the relay module's outputs (the normally open one and the ground) to the motherboard power pins

running some code on rPi

We can extend the power button functionality so Raspberry Pi can turn on and off our computer, while also still keeping the power button working.

Which is what I use to remotely turn on my computer on, by SSHing to rPi and running a script to turn the PC on while I'm away from home.

Why not Wake-On-LAN?

Wake-On-LAN has restrictions which makes it not as reliable as it could be, for example:

The ability to wake from a hybrid shutdown state (S4) (aka Fast Startup) or a soft powered-off state (S5) is unsupported in Windows 8 and above

The code and explanation for it in Part 2, when I get to writing it.

[psychosomatica]

Natural Text to Speech (python)

Microsoft Edge has very good, natural-sounding Text to Speech voices used in its "Read Aloud" feature. You can utilize these voices using python and a module called edge-tts (edge text to speech) to generate audio files of whatever text you like, including subtitles (for free).

Install python if you haven't already

Use pip to install edge-tts in your favorite command line

Run the CLI commands or create a new .py file. I suggest starting with this basic audio generation example.

Replace the TEXT variable contents with your string of choice. I recommend creating separate .txt files then reading them with the program, which allows you to cleanly scale your program to generate multiple files at once. (Imagine crawling your Obsidian vault to generate audio versions of all of your files!)

Choose your voice. You can use the Read Aloud feature in Edge to try out the different available voices (their quality varies). Their programatic voice names are found here. Note that not all voices are available, depending on your region, and trying to use one out of your region will throw an error. Read the footnotes. I'm still early in testing the different voices, but if you want to start on the right foot, Ava, or "en-US-AvaMultilingualNeural" is very good.

If you run the above example, it will generate a file called test.mp3 in the directory of the .py file. It's definitely performant. I ran a ~10k word file and it took a couple of minutes to generate (~23 MB with a running length of 1 hour and 3 minutes).

Have fun!

Added voice samples for the two I like the most, Ava and Brian multilingual.

[shemsuji432]

Exploring Python: Features and Where It's Used

Python is a versatile programming language that has gained significant popularity in recent times. It's known for its ease of use, readability, and adaptability, making it an excellent choice for both newcomers and experienced programmers. In this article, we'll delve into the specifics of what Python is and explore its various applications.

What is Python?

Python is an interpreted programming language that is high-level and serves multiple purposes. Created by Guido van Rossum and released in 1991, Python is designed to prioritize code readability and simplicity, with a clean and minimalistic syntax. It places emphasis on using proper indentation and whitespace, making it more convenient for programmers to write and comprehend code.

Key Traits of Python :

Simplicity and Readability: Python code is structured in a way that's easy to read and understand. This reduces the time and effort required for both creating and maintaining software.

Python code example: print("Hello, World!")

Versatility: Python is applicable across various domains, from web development and scientific computing to data analysis, artificial intelligence, and more.

Python code example: import numpy as np

Extensive Standard Library: Python offers an extensive collection of pre-built libraries and modules. These resources provide developers with ready-made tools and functions to tackle complex tasks efficiently.

Python code example: import matplotlib.pyplot as plt

Compatibility Across Platforms: Python is available on multiple operating systems, including Windows, macOS, and Linux. This allows programmers to create and run code seamlessly across different platforms.

Strong Community Support: Python boasts an active community of developers who contribute to its growth and provide support through online forums, documentation, and open-source contributions. This community support makes Python an excellent choice for developers seeking assistance or collaboration.

Where is Python Utilized?

Due to its versatility, Python is utilized in various domains and industries. Some key areas where Python is widely applied include:

Web Development: Python is highly suitable for web development tasks. It offers powerful frameworks like Django and Flask, simplifying the process of building robust web applications. The simplicity and readability of Python code enable developers to create clean and maintainable web applications efficiently.

Data Science and Machine Learning: Python has become the go-to language for data scientists and machine learning practitioners. Its extensive libraries such as NumPy, Pandas, and SciPy, along with specialized libraries like TensorFlow and PyTorch, facilitate a seamless workflow for data analysis, modeling, and implementing machine learning algorithms.

Scientific Computing: Python is extensively used in scientific computing and research due to its rich scientific libraries and tools. Libraries like SciPy, Matplotlib, and NumPy enable efficient handling of scientific data, visualization, and numerical computations, making Python indispensable for scientists and researchers.

Automation and Scripting: Python's simplicity and versatility make it a preferred language for automating repetitive tasks and writing scripts. Its comprehensive standard library empowers developers to automate various processes within the operating system, network operations, and file manipulation, making it popular among system administrators and DevOps professionals.

Game Development: Python's ease of use and availability of libraries like Pygame make it an excellent choice for game development. Developers can create interactive and engaging games efficiently, and the language's simplicity allows for quick prototyping and development cycles.

Internet of Things (IoT): Python's lightweight nature and compatibility with microcontrollers make it suitable for developing applications for the Internet of Things. Libraries like Circuit Python enable developers to work with sensors, create interactive hardware projects, and connect devices to the internet.

Python's versatility and simplicity have made it one of the most widely used programming languages across diverse domains. Its clean syntax, extensive libraries, and cross-platform compatibility make it a powerful tool for developers. Whether for web development, data science, automation, or game development, Python proves to be an excellent choice for programmers seeking efficiency and user-friendliness. If you're considering learning a programming language or expanding your skills, Python is undoubtedly worth exploring.

[michaelgogins]

More Notes on the Computer Music Playpen

I have finished maintenance on the VST3 plugin opcodes for Csound, Csound for Android, and some other things, and am re-focusing in composition.

One thing that happened as I was cleaning up the VST3 opcodes is that I discovered a very important thing. There are computer music programs that function as VST3 plugins and that significantly exceed the quality or power what Csound has so far done on it own, just for examples that I am using or plan to use:

The Valhalla reverbs by Sean Costello -- I think these actually derive from a reverb design that Sean did in the 1990s when he and I both were attending the Woof meetings at Columbia University. Sean's reverb design was ported first to Csound orchestra code, and then to C as a built-in opcode. It's the best and most widely used reverb in Csound, but it's not as good as the Valhalla reverbs, partly because the Valhalla reverbs can do a good job of preserving stereo.

Cardinal -- This is a fairly complete port of the VCV Rack "virtual Eurorack" patchable modular synthesiser not only to a VST3 plugin, but also to a WebAssembly module. This is exactly like sticking a very good Eurorack synthesizer right into Csound.

plugdata -- This is most of Pure Data, but with a slightly different and somewhat smoother user interface, as a VST3 plugin.

I also discovered that some popular digital audio workstations (DAWs), the workhorses of the popular music production industry, can embed algorithmic composition code written in a scripting language. For example, Reaper can host scripts written in Lua or Python, both of which are entirely capable of sophisticated algorithmic composition, and both of which have Csound APIs. And of course any of these DAWs can host Csound in the form of a Cabbage plugin.

All of this raises for me the question: What's the playpen? What's the most productive environment for me to compose in? Is it a DAW that now embeds my algorithms and my Csound instruments, or is it just code?

Right now the answer is not simply code, but specifically HTML5 code. And here is my experience and my reasons for not jumping to a DAW.

I don't want my pieces to break. I want my pieces to run in 20 years (assuming I am still around) just as they run today. Both HTML5 and Csound are "versionless" in the sense that they intend, and mostly succeed, in preserving complete backwards compatibility. There are Csound pieces from before 1990 that run just fine today -- that's over 33 years. But DAWs, so far, don't have such a good record in this department. I think many people find they have to keep porting older pieces to keep then running in newer software.

I'm always using a lot of resources, a lot of software, a lot of libraries. The HTML5 environment just makes this a great deal easier. Any piece of software that either is written in JavaScript or WebAssembly, or provides a JavaScript interface, can be used in a piece with a little but of JavaScript glue code. That includes Csound itself, my algorithmic composition software CsoundAC, the live coding system Strudel, and now Cardinal.

The Web browser itself contains a fantastic panoply of software, notably WebGL and WebAudio, so it's very easy to do visual music in the HTML5 environment.

[dhivyakrishnan107667]

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As the use of big data becomes more prevalent, ethical concerns surrounding privacy, security, and bias arise. Our course dedicates a comprehensive module to explore the ethical considerations in big data analytics. By understanding the impact of your work on individuals and society, you learn how to ensure responsible data handling and adhere to legal and ethical guidelines. By fostering a sense of responsibility, the course empowers you to embrace ethical practices and make a positive contribution to the industry.

Education and Learning Experience

Expert Instructors

Our Big Data Analytics Course is led by accomplished industry experts with a wealth of experience in the field. These instructors possess a deep understanding of big data analytics and leverage their practical knowledge to deliver engaging and insightful lessons. Their guidance and mentorship ensure you receive top-quality education that aligns with industry best practices, optimally preparing you for the challenges and opportunities that lie ahead.

Interactive and Collaborative Learning

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Flexible Learning Options

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The future is now, and big data analytics has the potential to transform your career. By enrolling in our Big Data Analytics Course at ACTE institute, you gain the necessary knowledge and skills to excel in this rapidly evolving field. From the incredible benefits and the wealth of information provided to the exceptional education and learning experience, our course equips you with the tools you need to thrive in the data-driven world of the future. Don't wait - take the leap and embark on an exciting journey towards a successful and fulfilling career in big data analytics.

[daemonhxckergrrl]

do you have rofi config recommendations for the coding-illiterate? :3

if you're looking for a nice theme i would recommend something like this catppuccin one (one of my fave colourschemes) :3 i think other popular colourschemes have rofi themes too !

the instructions are straightforward and at most require changing two config options as explained on the page.

if you're interested in using rofi for more advanced stuff there's a "not really programming" way and a "basic programming" way.

there's plenty of custom scripts by other people that use rofi's dmenu mode (the rough format is input_command | rofi -dmenu --other-options | output_command) and all you have to do is run the script/bind it to a shortcut.

the method i use does involve coding but nothing advanced - in fact the github page for it has some good examples ! this is a python module that lets you write a python script to interact with rofi. you can install it from the github or via pip (python's package manager).

a simple script to pick favourite programs from a list might something look like this:

#importing the modules for running commands and using rofi import os import sys from rofi import Rofi

r = Rofi() #setting our label string and our lists for options to select from and commands to run after selecting label = "Pinned Programs:" options = ['browser', 'discord', 'files', 'editor'] commands = ['firefox', 'flatpak run com.discordapp.Discord', 'nemo', 'gedit'] #the magic rofi command ! ^w^ opens a rofi window in selection mode, showing the label and options we set earlier, and returns us an index (from 0) for which selection was chosen index, key = r.select(label,options) #runs the command located in our commands list, using the index from our rofi command os.system(commands[index])

my personal script does a more complex version of this (and needs tidying bc it's hella messy lmao) but i hope some of this is useful ? and if not then lmk what else i can do to help :3

[aaksconsulting]

Mastering Real Multithreading In Python - Tips And Tricks For Optimal Performance

Are you tired of slow and unresponsive Python programs? Are you ready to take your programming skills to the next level by mastering real multithreading in Python? Look no further!

In this blog post, we will share tips and tricks for achieving optimal performance with real multithreading. Whether you’re a beginner or an experienced developer, these techniques will help you elevate your programming game and create lightning-fast applications that impress even the toughest critics. So let’s dive in and become masters of real multithreading in Python!

INTRODUCTION TO MULTITHREADING IN PYTHON

Multithreading is a powerful tool that can help you write more efficient code. In Python, the standard library provides the threading module, which allows you to create and work with threads. In this article, we’ll look at some tips and tricks for working with threads in Python.

First, let’s take a look at what a thread is. A thread is simply a unit of execution. When you create a new thread, it starts running in parallel with the main thread of your program. This can be useful when you have tasks that are independent of each other and can be run concurrently.

For example, let’s say you’re writing a program that downloads files from the web. You could have one thread responsible for downloading the files, while another thread handles extracting data from the downloaded files. By using two threads, you can make better use of your computer’s resources and potentially speed up the overall execution of your program.

Of course, working with threads also comes with its own set of challenges. For example, if two threads try to access the same data at the same time, they may end up corrupting that data. To avoid this, we need to use synchronization primitives such as locks and semaphores. We’ll discuss these later on in the article.

Now that we know what threads are and why they can be useful, let’s look at how to create and work with them in Python.

OVERVIEW OF MULTITHREADING BENEFITS

Python’s “threading” module allows for the creation of threads within a Python program. These threads can run concurrently, which can lead to performance gains on multicore processors. Multithreading can also be used to improve responsiveness in GUI applications.

There are several benefits to using multithreading in Python programs:

Concurrency: Threads can run concurrently, which can lead to more efficient use of processor resources on multicore processors.

Improved responsiveness: Threads can be used to improve the responsiveness of GUI applications by running tasks in the background while the main thread continues to process user input.

Better utilization of resources: Threads can be used to better utilize system resources such as network and I/O devices. By running multiple threads, these resources can be shared among the various threads and utilized more efficiently.

Reduced latency: Threads can be used to reduce latency in applications that need to perform time-sensitive tasks. By running multiple threads, tasks can be executed in parallel, which can lead to shorter overall execution times.

TYPES OF THREADS AND HOW TO CREATE THEM IN PYTHON

There are two types of threads in Python: the main thread and daemon threads. The main thread is the one that starts when the program begins execution. Daemon threads are created by the main thread and run in the background. They are used to perform tasks such as garbage collection and logging.

Threads can be created in Python using the threading module. To create a thread, you need to instantiate a Thread object. The constructor takes an optional argument, which is a function that will be run by the thread. If no function is provided, the thread will simply exit when it is started.

Once you have created a Thread object, you can start it by calling its start() method. This will cause the function that was passed to the constructor to be executed by the thread. If no function was passed, the thread will simply exit when it is started.

If you want to wait for a thread to finish before continuing execution of your program, you can call its join() method. This will block until the thread has finished running. Note that if you try to join() a daemon thread, your program will never terminate since daemon threads do not ever finish running (unless they are terminated with an unhandled exception).

SYNCHRONIZATION TECHNIQUES

There are many synchronization techniques that can be used to achieve optimal performance in Python. Here are some tips and tricks to help you get the most out of your multithreading applications:

1. Use locks wisely. Locks are a necessary evil in multithreaded programming. They are useful for protecting critical sections of code, but they can also lead to deadlocks if not used correctly. When using locks, always try to acquire them in the same order to avoid deadlocks. 2. Use thread-safe data structures. Some data structures, such as lists and dictionaries, are not thread-safe. This means that if multiple threads try to access and modify them concurrently, strange things can happen. To avoid this, you can use thread-safe versions of these data structures, such as the Queue class from the standard library. 3. Use the new asyncio module. The asyncio module was added in Python 3.4 and it provides a powerful framework for writing concurrent code using coroutines. If you’re targeting Python 3.4 or newer, this is definitely the way to go for optimal performance.

KEY PERFORMANCE OPTIMIZATIONS FOR MULTITHREADED APPLICATIONS

Python’s standard library provides a number of synchronization primitives including locks, semaphores, and events. In this section, we’ll cover some key performance optimizations that can be made when using these synchronization primitives in multithreaded applications.

One optimization that can be made is to use a lock object’s acquire() method with the blocking argument set to False . This will cause the acquire() method to return immediately if the lock is already held by another thread. If the lock is not available, then the current thread will continue executing without blocking. This can be useful in situations where it is not critical for the current thread to acquire the lock.

Another optimization that can be made is to use a semaphore object’s release() method with the count argument set to a value greater than 1 . This will release the semaphore multiple times, which can be helpful in situations where multiple threads are waiting on the semaphore. Releasing the semaphore multiple times can help to avoid unnecessary context switches between threads.

It is important to note that using too many synchronization primitives can actually hurt performance. When used excessively, synchronization primitives can introduce a significant amount of overhead into an application. Therefore, it is important to use them judiciously and only when absolutely necessary.

DEBUGGING TIPS AND PRACTICES

When it comes to debugging multithreaded Python applications, there are a few practices that can make your life much easier. Firstly, it’s important to understand the basics of the Python threading model. The Python Global Interpreter Lock (GIL) ensures that only one thread can execute Python code at a time. This means that if you’re trying to debug a multithreaded application, you need to be aware of the potential for threads to block each other.

Another important practice is to use a tool like pdb or ipdb when debugging multithreaded applications. These tools allow you to set breakpoints in your code and inspect the state of your application at those points. This can be extremely helpful in understanding what is happening in your code and why it is not working as expected.

It’s often useful to run your application under a profiler like Profile or pyprof2calltree. This can help you identify which parts of your code are taking up the most time, which can be helpful in pinpointing areas that need optimization.

CONCLUSION

We have come to the end of our discussion on mastering real multithreading in Python. With these tips and tricks, you will be able to optimize your code for maximum performance. Working with threads can be tricky, so it is important to understand the fundamentals before diving into complex operations like thread pooling or synchronization.

If you use these techniques correctly, you can drastically improve your program’s execution time and maintain a level of concurrency that suits your needs perfectly.