semvec/pypi-packages huggingface dataset:
requests-random-user-agent 2023.10.25
Automatically generate a random User Agent for the requests library

# requests-random-user-agent [![Build Status](https://travis-ci.org/DavidWittman/requests-random-user-agent.svg?branch=master)](https://travis-ci.org/DavidWittman/requests-random-user-agent) [![PyPI](https://img.shields.io/pypi/v/requests-random-user-agent.svg)](https://pypi.org/project/requests-random-user-agent/)

Configures the requests library to randomly select a desktop User-Agent. See the full list in [requests_random_user_agent/useragents.txt](requests_random_user_agent/useragents.txt).

### Installation

```
pip install requests-random-user-agent
```

### Usage

``` python
import requests
import requests_random_user_agent

s = requests.Session()
print(s.headers['User-Agent'])

# Without a session
resp = requests.get('https://httpbin.org/user-agent')
print(resp.json()['user-agent'])
```

User-Agents are randomized per-session or per-request. Individual HTTP requests without a session will each have a random User-Agent selected from the list in useragents.txt.

#### Android User-Agents

Set the environment variable `UA_PLATFORM=android` to use a list of [Android-specific User-Agents](requests_random_user_agent/android_useragents.txt) instead. List provided by [WhatIsMyBrowser](whatismybrowser.com).
mau_tex_visitor 2.0.1
A visitor for Mau that converts the AST into TeX
Requires Python: >=3.7

# Mau TeX Visitor

This is a plugin for the [Mau](https://github.com/Project-Mau/mau) markup language. The plugin provides the conversion from Mau source to TeX.

You can install this plugin with

```
pip install mau-tex-visitor
```

and Mau will automatically be able to load it. To use the visitor you need to load it and to pass it to the class `Mau` when you instantiate it

``` python
from mau import Mau, load_visitors

visitor_classes = load_visitors()

visitors = {i.format_code: i for i in visitor_classes}
visitor_class = visitors["tex"]

mau = Mau(
    "path/of/the/source",
    visitor_class=visitor_class,
)

lexer = self._mau.run_lexer(text)
parser = self._mau.run_parser(lexer.tokens)
content = self._mau.process(parser.nodes, parser.environment)

if visitor_class.transform:
    content = visitor_class.transform(content)
```

The default extension for templates is `.tex`.
mau_html_visitor 2.0.2
A visitor for Mau that converts the AST into HTML
Requires Python: >=3.7

# Mau HTML Visitor

This is a plugin for the [Mau](https://github.com/Project-Mau/mau) markup language. The plugin provides the conversion from Mau source to HTML.

You can install this plugin with

```
pip install mau-html-visitor
```

and Mau will automatically be able to load it. To use the visitor you need to load it and to pass it to the class `Mau` when you instantiate it

``` python
from mau import Mau, load_visitors

visitor_classes = load_visitors()

visitors = {i.format_code: i for i in visitor_classes}
visitor_class = visitors["html"]

mau = Mau(
    "path/of/the/source",
    visitor_class=visitor_class,
)

lexer = self._mau.run_lexer(text)
parser = self._mau.run_parser(lexer.tokens)
content = self._mau.process(parser.nodes, parser.environment)

if visitor_class.transform:
    content = visitor_class.transform(content)
```

The default extension for templates is `.html`. The plugin uses [Pygments](https://pygments.org/) to provide source code highlighting.
terai 1.0.0
A terminal-based AI assistant that translates natural language queries into shell commands and executes them directly. Users can interact naturally, such as by typing "create me a Docker container," and the AI will generate, display, and run the appropriate command.
Requires Python: <4.0,>=3.12

# Terminal AI

Terminal AI is a command-line interface (CLI) application that generates terminal commands based on user queries using API.

## Installation

```
pipx install terminal-ai
```

## Usage

1. Ensure you have set the `GROQ_API_KEY` environment variable:

    ```sh
    export GROQ_API_KEY=<your_api_key>
    ```

2. Run the application with a query:

    ```sh
    tai build a docker container
    ```

    Alternatively, you can run the script directly:

    ```sh
    python -m tai build a docker container
    ```

## Example

```sh
$ poetry run tai list files in the current directory
Command: ls
Press Enter to execute the command, or press 'q' to cancel:
topsis-Sarabjeet-102203770 1.1.0
A Python package implementing TOPSIS method for Multi-Criteria Decision Making (MCDM)
Requires Python: >=3.6

# TOPSIS Python Package

A Python package that implements the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method. This package helps in solving Multi-Criteria Decision Making (MCDM) problems by ranking alternatives based on multiple criteria.

## Installation

```bash
pip install topsis-Sarabjeet-102203770
```

## Usage

The package can be used both as a command-line tool and as a Python module.

### Command Line Usage

```bash
python -m topsis <InputDataFile> <Weights> <Impacts> <ResultFileName>
```

Example:
```bash
python -m topsis data.xlsx "1,1,1,1,1" "+,-,+,-,+" result.csv
```

### Input File Format

- Excel file with .xlsx extension
- First column: Fund/Alternative names
- 2+ columns: Numerical values for criteria
- Example:
  ```
  Fund Name | Return | Risk | Expense | ...
  Fund A    | 10     | 20   | 30     | ...
  Fund B    | 15     | 25   | 35     | ...
  ```

### Parameters

- Weights: Comma-separated values of weights (e.g., "1,1,1,1,1")
- Impacts: Comma-separated +/- signs for maximizing/minimizing criteria (e.g., "+,-,+,-,+")
- ResultFileName: Output CSV file name

### Output

The program will create a CSV file containing the input data along with:
- Topsis Score
- Rank

## Sample Python Usage

```python
from topsis import topsis

topsis("input.xlsx", [1,1,1,1,1], ['+','-','+','-','+'], "output.csv")
```

## License

This project is licensed under the MIT License - see the LICENSE file for details.

## Author

Sarabjeet Singh (ssingh20_be22@thapar.edu)
pyqgraf 0.0.19
PyQgraf is a Python wrapper for Qgraf, a Feynman diagram generator.
Requires Python: >=3.8

# pyqgraf
Simplified plotting and fitting in python.

[![PyPI version][pypi image]][pypi link] [![PyPI version][pypi versions]][pypi link] ![downloads](https://img.shields.io/pypi/dm/pyqgraf.svg)

 [![test][a t image]][a t link] [![Coverage Status][c t i]][c t l] [![Documentation][rtd t i]][rtd t l]

## Documentation

## Requirements

 * build-essential (or just gfortran)
 * ninja-build (maybe cmake works too) 

## Versions

### Stable

```sh
pip install pyqgraf
```

Optional: --user or --upgrade

### Dev

```sh
pip install --index-url https://test.pypi.org/simple/ pyqgraf
```

[doc stable]: https://apn-pucky.github.io/pyqgraf/index.html
[doc test]: https://apn-pucky.github.io/pyqgraf/test/index.html

[pypi image]: https://badge.fury.io/py/pyqgraf.svg
[pypi link]: https://pypi.org/project/pyqgraf/
[pypi versions]: https://img.shields.io/pypi/pyversions/pyqgraf.svg

[a s image]: https://github.com/APN-Pucky/pyqgraf/actions/workflows/stable.yml/badge.svg
[a s link]: https://github.com/APN-Pucky/pyqgraf/actions/workflows/stable.yml
[a t link]: https://github.com/APN-Pucky/pyqgraf/actions/workflows/test.yml
[a t image]: https://github.com/APN-Pucky/pyqgraf/actions/workflows/test.yml/badge.svg

[cc s q i]: https://app.codacy.com/project/badge/Grade/38630d0063814027bd4d0ffaa73790a2?branch=stable
[cc s q l]: https://www.codacy.com/gh/APN-Pucky/pyqgraf/dashboard?utm_source=github.com&amp;utm_medium=referral&amp;utm_content=APN-Pucky/smpl&amp;utm_campaign=Badge_Grade?branch=stable
[cc s c i]: https://app.codacy.com/project/badge/Coverage/38630d0063814027bd4d0ffaa73790a2?branch=stable
[cc s c l]: https://www.codacy.com/gh/APN-Pucky/pyqgraf/dashboard?utm_source=github.com&utm_medium=referral&utm_content=APN-Pucky/smpl&utm_campaign=Badge_Coverage?branch=stable

[cc q i]: https://app.codacy.com/project/badge/Grade/38630d0063814027bd4d0ffaa73790a2
[cc q l]: https://www.codacy.com/gh/APN-Pucky/pyqgraf/dashboard?utm_source=github.com&amp;utm_medium=referral&amp;utm_content=APN-Pucky/smpl&amp;utm_campaign=Badge_Grade
[cc c i]: https://app.codacy.com/project/badge/Coverage/38630d0063814027bd4d0ffaa73790a2
[cc c l]: https://www.codacy.com/gh/APN-Pucky/pyqgraf/dashboard?utm_source=github.com&utm_medium=referral&utm_content=APN-Pucky/smpl&utm_campaign=Badge_Coverage

[c s i]: https://coveralls.io/repos/github/APN-Pucky/pyqgraf/badge.svg?branch=stable
[c s l]: https://coveralls.io/github/APN-Pucky/pyqgraf?branch=stable
[c t l]: https://coveralls.io/github/APN-Pucky/pyqgraf?branch=master
[c t i]: https://coveralls.io/repos/github/APN-Pucky/pyqgraf/badge.svg?branch=master

[rtd s i]: https://readthedocs.org/projects/pyqgraf/badge/?version=stable
[rtd s l]: https://pyqgraf.readthedocs.io/en/stable/?badge=stable
[rtd t i]: https://readthedocs.org/projects/pyqgraf/badge/?version=latest
[rtd t l]: https://pyqgraf.readthedocs.io/en/latest/?badge=latest
Topsis-AarushPruthi-102203793 1.0.2
A Python package for TOPSIS implementation, by Aarush Pruthi
Requires Python: >=3.6

# Topsis-Aarush-102203793

A Python package to perform the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for decision-making problems.

## Installation
```bash
pip install Topsis_AarushPruthi_102203793