semvec/pypi-packages huggingface dataset:
bg-samplesheet-file-utils 0.1.0
Collection of Python modules for reading and writing the comma-separated samplesheet file.
Requires Python: >=3.6
Keywords: bg_samplesheet_file_utils

=========================
BG Samplesheet File Utils
=========================

Collection of Python modules for reading and writing the comma-separated samplesheet file.


Features
--------

* Parser for reading the comma-separated samplesheet file
* Writer for writing the comma-separated samplesheet file
* Record class for representing a record in the samplesheet file

Credits
-------

This package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.

.. _Cookiecutter: https://github.com/audreyr/cookiecutter
.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage


=======
History
=======

0.1.0 (2024-10-16)
------------------

* First release on PyPI.
verificar-cpf 0.0.1
Verifica se o número do CPF é válido
Requires Python: >=3.8

# verificar_cpf

Description. 
The package verificar_cpf is used to:
    Inserir:
	    - Solicita que usuÃ¡rio digite o CPF.
    Verificar:
        - Calcula os dÃ­gitos verificadores.
        - Compara com os nÃºmeros do CPF informado e retorna se Ã© vÃ¡lido.

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install verificar_cpf

```bash
pip install verificar_cpf
```

## Author
Leonardo Diniz

## License
[MIT](https://choosealicense.com/licenses/mit/)
crop-people-cli 1.4.2
CLI tool to crop people from images using YOLOvX
Requires Python: >=3.12

# YOLOv8 Person Cropping Tool

This tool uses YOLOv8 to detect and crop persons from images. It's designed to process multiple images, optionally searching recursively through directories, and can crop either the largest detected person or all detected persons in each image.

## Features

- Uses YOLOv8 for accurate person detection
- Supports multiple YOLOv8 models (yolov8x.pt, yolov8m.pt, yolov8s.pt)
- Processes images in parallel for faster execution
- Allows recursive directory search
- Option to crop all detected persons or just the largest one
- Adds customizable margin to cropped images
- Saves images with no detected persons in a separate directory
- Option to remove small images after processing

## Installation

1. Clone this repository
2. Install the required dependencies:

```bash
pip install ultralytics rich typer opencv-python
```

3. Ensure you have the YOLOv8 models in your home directory under a `yolov8` folder.

## Usage

Run the script using the following command:

```bash
python main.py [OPTIONS]
```

### Options

- `--margin-percentage INTEGER`: Margin percentage for bounding box (default: 3, recommended range: 0-10)
- `--model-size INTEGER`: Model size (default: 640, recommended: 320, 640, or 1280)
- `--model TEXT`: YOLOv8 model to use (options: yolov8x.pt, yolov8m.pt, yolov8s.pt)
- `--recursive / --no-recursive`: Search for images recursively (default: False)
- `--crop-all / --no-crop-all`: Crop all detected persons instead of just the largest (default: False)

## How it works

1. The script prompts you to select an input directory.
2. It processes all images in the selected directory (and subdirectories if recursive option is enabled).
3. Detected persons are cropped from the images with the specified margin.
4. Cropped images are saved in a 'cropped' subdirectory within the input directory.
5. Images with no detected persons are saved in a 'no-person' subdirectory.
6. After processing, you have the option to remove small images based on a size threshold.
pyxbar 0.3.30
helper functions for xbar menus
Requires Python: >=3.9

Python library to make xbar development easy in python

Include this in your file after the regular imports

```py
def install(pkg: str, spec: str = "", cache_dir: Path | str = "~/.cache"):
    import os, sys, pip, importlib, importlib.metadata  # isort: skip # noqa: E401
    from pip._vendor.packaging.requirements import Requirement
    name, cache = Path(__file__), Path(os.environ.get("XDG_CACHE_HOME", cache_dir))
    sitep = (cache / f"pyxbar/{name.name}/site-packages").expanduser().as_posix()
    sys.path += [] if sitep in sys.path else [sitep]
    try:
        requirement = Requirement(spec or pkg)
        assert importlib.metadata.version(requirement.name) in requirement.specifier
    except Exception as e:
        pip.main(["install", "--upgrade", f"--target={sitep}", *(spec or pkg).split()])
        importlib.invalidate_caches()


# install the PyXbar library from pypi
install("pyxbar")

# use it!
from pyxbar import (
    Config,
    Divider,
    Menu,
    MenuItem,
    Renderable,
    ShellItem,
)


class MyConfig(Config):
    MYVARIABLE: bool = True
    ...

CONFIG = get_config(MyConfig)  # type: ignore




if __name__ == "__main__":
    Menu("some title").with_items(
        Divider(),  # shortcut for ---, also handles submenu depth
        MenuItem(
            "👁️ overview",
        ).with_submenu(
            MenuItem("hi, i'm a submenu!").
            MenuItem("hi, i'm monospace!", monospaced=True)
        ),
    ).print()

```
spideymaps 0.1.2
For generating heat maps of single-molecule localization data in rod-shaped bacteria.

# Spideymaps
A system for identifying patterns in single molecule experiments in rod-shaped bacteria.

## Installation
If git installed:  
~~~  
pip install git+https://github.com/d-foust/spideymaps.git#egg=spideymaps  
~~~  

If git is not installed, use "Download ZIP" and unpack folder somewhere convenient. Navigate to folder and run:
~~~  
pip install -e .  
~~~