semvec/pypi-packages huggingface dataset:
classutil 2.0.3
Classutil scraper for UNSW.
License: MIT
Requires Python: >=3
Keywords: unsw classutil scraper

classutil scraper
=================

My attempt at a [classutil](http://classutil.unsw.edu.au). It downloads the current UNSW class
allocations into a JSON file.

## Installation
```bash
# optional: use virtualenv
virtualenv -p python3 venv
. venv/bin/activate

pip3 install -r requirements.txt
```
## Run
```sh
python -m classutil output.json
```

The options are configurable, run with `--help` for more options.


## Library Usage
```py3
from classutil import scrape

# Scrape data
# Arguments don't need to be specified as these are the defaults.
data = scrape(
	root="https://classutil.unsw.edu.au",
	concurrency=1,
	logging=False)
```
Topsis-Sanchit-101917123 0.0.2
A package -> Calculates Topsis Score and Rank them accordingly
License: MIT

## topis_sanchit

# TOPSIS

Submitted By: **Sanchit Bhadgal - 101917123**.

Type: **Package**.

Title: **TOPSIS method for multiple-criteria decision making (MCDM)**.

Version: **1.0.0**.

Date: **2022-02-26**.

Author: **Sanchit Bhadgal**.

Maintainer: **Sanchit Bhadgal <sanchitbhadgal36@gmail.com>**.

Description: **Evaluation of alternatives based on multiple criteria using TOPSIS method.**.

---

## What is TOPSIS?

**T**echnique for **O**rder **P**reference by **S**imilarity to **I**deal **S**olution
(TOPSIS) originated in the 1980s as a multi-criteria decision making method.
TOPSIS chooses the alternative of shortest Euclidean distance from the ideal solution,
and greatest distance from the negative-ideal solution.

---

## How to install this package:

```
>> pip install Topsis-Sanchit-101917123
```

### In Command Prompt

```
>> topsis data.csv "1,1,1,1" "+,+,-,+" result.csv
```

## Input file (data.csv)

The decision matrix should be constructed with each row representing a Model alternative, and each column representing a criterion like Accuracy, R<sup>2</sup>, Root Mean Squared Error, Correlation, and many more.

| Model | Correlation | R<sup>2</sup> | RMSE | Accuracy |
| ----- | ----------- | ------------- | ---- | -------- |
| M1    | 0.79        | 0.62          | 1.25 | 60.89    |
| M2    | 0.66        | 0.44          | 2.89 | 63.07    |
| M3    | 0.56        | 0.31          | 1.57 | 62.87    |
| M4    | 0.82        | 0.67          | 2.68 | 70.19    |
| M5    | 0.75        | 0.56          | 1.3  | 80.39    |

Weights (`weights`) is not already normalised will be normalised later in the code.

Information of benefit positive(+) or negative(-) impact criteria should be provided in `impacts`.

---

## Output file (result.csv)

| Model | Correlation | R<sup>2</sup> | RMSE | Accuracy | Topsis_score | Rank |
| ----- | ----------- | ------------- | ---- | -------- | ------------ | ---- |
| M1    | 0.79        | 0.62          | 1.25 | 60.89    | 0.7722       | 2    |
| M2    | 0.66        | 0.44          | 2.89 | 63.07    | 0.2255       | 5    |
| M3    | 0.56        | 0.31          | 1.57 | 62.87    | 0.4388       | 4    |
| M4    | 0.82        | 0.67          | 2.68 | 70.19    | 0.5238       | 3    |
| M5    | 0.75        | 0.56          | 1.3  | 80.39    | 0.8113       | 1    |

---

The output file contains columns of input file along with two additional columns having __Topsis_score__ and __Rank__
toontown.py 2.0.1
A simple Python wrapper for the Toontown Rewritten API
License: MIT
Requires Python: >=3.8.0

# toontown.py
A simple Python API wrapper for the Toontown Rewritten/Corporate Clash API

## Features
- Asynchronous and synchronous
- API complete

## Installing
**Python 3.8 or higher is required**

```zsh
# Linux/macOS
python3 -m pip install -U toontown.py

# Windows
py -3 -m pip install -U toontown.py
```

## About

All methods return a tuple-like wrapper class with all the response data wrapped in objects (except Login and Status objects)

e.g. This will print all the news article URLs for Rewritten and Corporate Clash

```py
async def main():
    """Example main function"""
    session = aiohttp.ClientSession(raise_for_status=True)

    async with RewrittenAsyncToontownClient(session=session) as ttr_client, ClashAsyncToontownClient(session=session) as clash_client:
        news_list = await ttr_client.news(all=True)

        for news in news_list:
            print(news.article_url)

        news_list = await clash_client.news()

        for news in news_list:
            print(news.article_url)
```