semvec/pypi-packages huggingface dataset:
calculator-pkg-yourusername 0.1.0
Простой калькулятор на Python
Requires Python: >=3.6

Простой калькулятор на Python, предоставляющий базовые и расширенные математические операции.

## Установка

```bash
pip install calculator-pkg-yourusername
```

## Использование

```python
from calculator.basic import add, subtract
from calculator.advanced import power, square_root

print(add(2, 3))             # Вывод: 5
print(subtract(5, 2))        # Вывод: 3
print(power(2, 3))           # Вывод: 8
print(square_root(16))       # Вывод: 4.0
```
## Лицензия
bg-scrambler 1.0.2
A simple tool to scramble the background (=anything but people) in images.
Requires Python: >=3.9
Keywords: Ultralytics, Computer Vision, Image Processing, YOLO

# Background Scrambler
This package contains a simple tool that allows to scramble the background of images. By background, we mean anything but the people in the image. This is practical for, e.g., psychology or neuroscience experiments dealing with human vision and/or social cognition.

As an example, it turns this image:

<div align="center">
<img src="https://raw.githubusercontent.com/LaurentMertens/BgScrambler/refs/heads/main/bg_scrambler/test_image_unsplash.jpg" width="640px">
</div>

into this image:
<div align="center">
<img src="https://raw.githubusercontent.com/LaurentMertens/BgScrambler/refs/heads/main/bg_scrambler/res_image.jpg" width="640px">
</div>

## Installation
To install this application, either clone this repository, or simply install using
```pip install bg-scrambler```.

## Usage
The application will keep the original image size, if its largest side is a multiple of 32 (a requisite of YOLOv11),
up till sizes of 2016px. If the largest side exceeds 2016, the image will be resized so that this side becomes 2016px.

Note that the scrambler returns the processed image. By default, the output is NOT saved to disk.\
To save the output (i.e., the processed image) to disk, specify ```out_file='path/to/output.jpg'``` (or ```png```) when calling ```process_img()```.

### When cloning the repo
See the example in ```scrambler.py```, reprised here:
```python
if __name__ == '__main__':
    # Create Scrambler instance
    proc = Scrambler()
    # Replace the line below to point at the image you want to parse
    _img = 'test_image_unsplash.jpg'
    # Parse image
    # block_size: size of the blocks (or patches if you prefer) that will be randomly swapped around
    # blur_radius: size of the blur to be applied to the scrambled part
    res = proc.process_img(_img, block_size=3, blur_radius=0, out_file=None, b_show=True)
```

### When installing the package through pip:
A short demo:
```python
from bg_scrambler import scrambler
import os


if __name__ == '__main__':
    proc = scrambler.Scrambler()
    _img = os.path.join(os.path.dirname(scrambler.__file__), 'test_image_unsplash.jpg')

    # Parse image
    # block_size: size of the blocks (or patches if you prefer) that will be randomly swapped around
    # blur_radius: size of the blur to be applied to the scrambled part
    res = proc.process_img(_img, block_size=3, blur_radius=0, out_file=None, b_show=True)
```

## Licensing
This repository is made available under an MIT license (see [LICENSE.md](./LICENSE.md)).
This is in agreement with the original repository, which also uses an MIT license.

The test image is taken from Unsplash:\
[https://unsplash.com/photos/a-person-sitting-on-a-couch-with-a-laptop-X1GZqv-F7Tw](https://unsplash.com/photos/a-person-sitting-on-a-couch-with-a-laptop-X1GZqv-F7Tw)\
The image is distributed under the [Unsplash license](https://unsplash.com/license).

Author: Laurent Mertens\
Mail: [contact@laurentmertens.com](contact@laurentmertens.com)
pythonalsa 0.1.2
ALSA C library bindings using ctypes for Python
Requires Python: >=3.9
Keywords: alsa, audio, linux, mixer, sound

# pythonalsa

Python bindings for ALSA (Advanced Linux Sound Architecture) using ctypes. Control audio mixers, volume levels, and mute states directly from Python without external dependencies.

## Features

- Pure Python implementation using ctypes (no compiled extensions)
- Control volume levels (raw values and percentages)
- Mute/unmute audio channels
- List all available sound cards and mixer elements
- Clean, Pythonic API with context managers

## Requirements

- Python 3.9 or higher
- Linux system with ALSA (`libasound.so.2`)

## Installation

```bash
pip install pythonalsa
```

## Usage

```python
from pythonalsa import Card, Mixer, list_cards

# Get the default card
card = Card()
print(f"Default card: {card.name}")

# Or get a specific card
card = Card(0)

# List all cards
for card in list_cards():
    print(f"Card {card.index}: {card.name}")

# List mixers for a card
for mixer in card.list_mixers():
    print(f"Mixer: {mixer.name}")

# Get a specific mixer
mixer = card.get_mixer("Master")

# Or create mixer directly
mixer = Mixer(card=card, name="Master")

# Volume control
print(f"Current volume: {mixer.volume_percent}%")
mixer.volume_percent = 75

# Raw volume
min_vol, max_vol = mixer.volume_range
print(f"Volume range: {min_vol} - {max_vol}")
mixer.volume = 23

# Mute control
print(f"Muted: {mixer.muted}")
mixer.muted = True
mixer.muted = False
```

## API Reference

See docstrings for `Card`, `Mixer`, `iter_cards`, and `ALSAError`.

## License

GPL-3.0-or-later

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.
harlequin-odbc 0.4.0
A Harlequin adapter for ODBC drivers.
Requires Python: >=3.10

# harlequin-odbc

This repo provides the ODBC adapter for Harlequin.

## Installation

`harlequin-odbc` depends on `harlequin`, so installing this package will also install Harlequin.

### Pre-requisites

You will need an ODBC driver manager installed on your OS. Windows has one built-in, but for Unix-based OSes, you will need to download and install one before installing `harlequin-odbc`. You can install unixODBC with `brew install unixodbc` or `sudo apt install unixodbc`. See the [pyodbc docs](https://github.com/mkleehammer/pyodbc/wiki/Install) for more info.

Additionally, you will need to install the ODBC driver for your specific database (e.g., `ODBC Driver 18 for SQL Server` for MS SQL Server). For more information, see the docs for your specific database.

### Using pip

To install this adapter into an activated virtual environment:
```bash
pip install harlequin-odbc
```

### Using poetry

```bash
poetry add harlequin-odbc
```

### Using pipx

If you do not already have Harlequin installed:

```bash
pip install harlequin-odbc
```

If you would like to add the ODBC adapter to an existing Harlequin installation:

```bash
pipx inject harlequin harlequin-odbc
```

### As an Extra
Alternatively, you can install Harlequin with the `odbc` extra:

```bash
pip install harlequin[odbc]
```

```bash
poetry add harlequin[odbc]
```

```bash
pipx install harlequin[odbc]
```

## Usage and Configuration

You can open Harlequin with the ODBC adapter by selecting it with the `-a` option and passing an ODBC connection string:

```bash
harlequin -a odbc 'Driver={ODBC Driver 18 for SQL Server};Server=tcp:harlequin-example.database.windows.net,1433;Database=dev;Uid=harlequin;Pwd=my_secret;Encrypt=yes;TrustServerCertificate=no;Connection Timeout=30;'
```

The ODBC adapter does not accept other options.

For more information, see the [Harlequin Docs](https://harlequin.sh/docs/odbc/index).