semvec/pypi-packages huggingface dataset:
kattis-problem-setup 1.0.3
View information on Kattis problems and download sample data.
Keywords: kattis parsing beautifulsoup

# kattis-problem-setup

Download the sample data for any problems from [Kattis](https://open.kattis.com/).

## Requirements

The project is built in [Python 3](https://www.python.org/).

## Installation

To install, simply clone the project:

```shell_script
git clone https://github.com/bradendubois/kattis-problem-setup
cd kattis-problem-setup
```

## Usage

To run the project, simply run the ``main.py`` either directly, or in Python, with any number of problem IDs provided.

```shell_script
./main.py PROBLEM_1 PROBLEM_1
```

or

```shell_script
python main.py PROBLEM_1 PROBLEM_2
```

where ``PROBLEM`` would be the ID of a problem. 

**Example**: If there were sample material for the problem [Hello World!](https://open.kattis.com/problems/hello), where the ID is ``hello``, usage would be:

```shell_script
pythom main.py hello
```

and output would be:

```
Parsing: hello 

Title: Hello World!
ID: hello
CPU Time Limit: 5 seconds
Memory Limit: 1024 MB
Difficulty: 1.2
```

## Disclaimers

**The description of each problem is not downloaded.**

Kattis [Terms of Service](https://open.kattis.com/help/tos) is clear (see ``2. License for content``); materials cannot be (a) *copied* or (b) *publically displayed*. This script gets uninformative information on memory limits / CPU time, etc. but does not represent materials concerning the *problem itself*. It would be a clear violation of the TOS to copy, or likely facilitate copying, the actual description of each problem. 

**Can it copy all the problems from Kattis?**

It could be easily made to download a description of every problem from Kattis, but for the same reasons as above, it will not be implemented.

## Acknowledgements

- [Kattis](https://open.kattis.com/), for hosting fun competitive programming problems.
- [Beautiful Soup](https://www.crummy.com/software/BeautifulSoup/), still the best choice for web scraping in Python.
pdlpy 0.4.1
Probability Distribution Library for Python

# Probability Distribution Library for Python

[![PyPI](https://img.shields.io/pypi/v/pdlpy)](https://pypi.org/project/pdlpy)
[![Python Version](https://img.shields.io/pypi/pyversions/pdlpy)](https://python.org)
[![Downloads](https://pepy.tech/badge/pdlpy)](https://pepy.tech/project/pdlpy)
[![GitHub Build](https://img.shields.io/github/actions/workflow/status/andrebienemann/pdlpy/ci.yml)](https://github.com/andrebienemann/pdlpy/actions/workflows/ci.yml)
[![Codecov](https://img.shields.io/codecov/c/github/andrebienemann/pdlpy)](https://app.codecov.io/gh/andrebienemann/pdlpy)
[![License](https://img.shields.io/github/license/andrebienemann/pdlpy)](https://github.com/andrebienemann/pdlpy/blob/main/LICENSE)
[![Code Style](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)

**Probability Distribution Library for Python** is a library for simulation and analysis of random processes.
It contains classes for descrete and continious distributions as listed below.

Discrete distributions:

- [Bernoulli](https://andrebienemann.github.io/pdlpy/discrete/bernoulli)
- [Binomial](https://andrebienemann.github.io/pdlpy/discrete/binomial)
- [Geometric](https://andrebienemann.github.io/pdlpy/discrete/geometric)
- [Hypergeometric](https://andrebienemann.github.io/pdlpy/discrete/hypergeometric)
- [Poisson](https://andrebienemann.github.io/pdlpy/discrete/poisson)

Continuous distributions:

- [Exponential](https://andrebienemann.github.io/pdlpy/continuous/exponential)
- [Normal](https://andrebienemann.github.io/pdlpy/continuous/normal)
- [Uniform](https://andrebienemann.github.io/pdlpy/continuous/uniform)

## Installation

Install and update using [pip](https://pip.pypa.io/en/stable/getting-started/):

```shell
pip install -U pdlpy
```

## Demo

Create and analyse probability distributions as simple as:

![demo](https://github.com/andrebienemann/pdlpy/blob/main/docs/assets/demo.gif)

## Links

- [Documentation](https://andrebienemann.github.io/pdlpy/)
- [PyPI Releases](https://pypi.org/project/pdlpy/)
- [Source Code](https://github.com/andrebienemann/pdlpy/)
- [Issue Tracker](https://github.com/andrebienemann/pdlpy/issues/)
TSADeval 0.0.2
A python module for evaluating time series anomaly detection algorithms
Requires Python: >=3.7

TSADeval is a python module  for evaluating Time Series Anomaly Detection. It
supports both common and time-series specific metrics applicable to any Time Series Anomaly Detection Algorithm evaluation.


# Installation
To install the module, run  `pip install TSADeval` in terminal.


# Import
After installation, run `from TSADeval import evalmetric` to import all metrics
discocrash.py 0.1.1
Module for creating discord crash bots

# discorash.py
This module can be used to create crash bots.



# Installing


```pip install discocrash.py```



# Example

```py
import disnake
from discocrash import crash
from disnake.ext import commands, tasks
import asyncio




intents = disnake.Intents.all()
bot = commands.Bot(command_prefix="your token", intents=intents)
bot.remove_command('help')
token = "your token"


@bot.command()
async def crush(ctx):
	await crash.crash(ctx, "crashed server name", "crash channel reason", "crash role reason", "crashed-channel-name", "crashed-role-name", 100, 10) #the penultimate parameter is responsible for how many channels will be created. The last parameter is responsible for how many roles will be created


bot.run(token)
ZenithLib 1.0.1
Zenith is a powerful Python framework that revolutionizes web development by enabling declarative and efficient UI components for building interactive and scalable websites.
Requires Python: >=3.7


<p align="center">
<img src="./docs/images/Upscaled-Zenith.png" style="align: center; width: 150px; text-align: center; display: flex;">
</p><br>

# Zenith
![Python Version](https://img.shields.io/badge/Python-3.8-brightgreen.svg)
![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)
![PyPI Version](https://img.shields.io/pypi/v/ZenithLib)
![Downloads](https://img.shields.io/pypi/dm/ZenithLib)
![GitHub Issues](https://img.shields.io/github/issues/Ryan-Rudd/Zenith)
![GitHub Pull Requests](https://img.shields.io/github/issues-pr/Ryan-Rudd/Zenith)

Zenith is a powerful Python framework that revolutionizes web development. It provides a declarative approach to designing efficient UI components, making it easier than ever to build interactive and scalable websites.

## Features

- **Efficient UI components**: Zenith makes it easy to build and manage reusable UI components.
- **Scalable**: Designed with large-scale applications in mind, Zenith helps you build websites that can grow with your needs.
- **Declarative**: Zenith's declarative style makes your code easier to understand and maintain.

## Getting Started

### Installation

To install Zenith, simply run:

```shell
pip install ZenithLib
```

# Basic Usage 

Here's a basic example of how to use Zenith to create a webpage:

```python
import Zenith

app = Zenith.WApp()

homePageContent = """
    <Container>
        <Header>Welcome to Zenith</Header>
        <Paragraph>A Python framework for web development</Paragraph>
    </Container>
"""

builder = Zenith.Builder({"home": homePageContent})
built = builder.Build()

style = Zenith.Stylesheet.new({
    'body': {
        'background-color': '#f8f9fa',
        'font-family': 'Arial, sans-serif',
    },
    'h1': {
        'color': '#007BFF',
        'font-size': '32px',
    },
    'p': {
        'color': '#6c757d',
        'font-size': '16px',
    },
})

style.apply(built, "home")
app.register_route('/', built['home'])
app.serve()

```

# Documentation

For more detailed information on using Zenith, please refer to our [documentation](/docs).

# Contributing 
We welcome contributions of all kinds. Check out our [contributing guide](./CONTRIBUTING.md) for more details on how you can help improve Zenith.

# License 

Zenith is released under the MIT License. See [LICENSE](./LICENSE) for details.

# Connect With Us
Have questions or suggestions? Please [open an issue on GitHub](https://github.com/Ryan-Rudd/Zenith/issues) or [send us a pull request](https://github.com/Ryan-Rudd/Zenith/pulls).