semvec/pypi-packages huggingface dataset:
albums-picker 1.0.1
Picks a random album from 1001 Albums You Must Hear Before You Die
Requires Python: >=3.8

[![MIT License][license-shield]][license-url]

<!-- Project Title and description -->
<br />
<div align="center">
  <a href="https://github.com/santi224m/1001-albums-picker">
    <img src="https://raw.githubusercontent.com/santi224m/1001-albums-picker/main/images/logo.png" alt="Logo" width="80" height="80">
  </a>

  <h3 align="center">Albums Picker</h3>

  <p align="center">
    Picks a random album from <a href="https://en.wikipedia.org/wiki/1001_Albums_You_Must_Hear_Before_You_Die">1001 Albums You Must Hear Before You Die</a>
    or from <a href="https://www.qobuz.com/us-en/awards/qobuz/discotheque_ideale/download-streaming-albums">The Qobuz Essential Discography</a>
  </p>
</div>

<!-- ABOUT THE PROJECT -->
## About The Project

![1001 Albums Picker Screen Shot][terminal-screenshot]

Running the program selects an album at random from the book [1001 Albums You Must Hear Before You Die](https://en.wikipedia.org/wiki/1001_Albums_You_Must_Hear_Before_You_Die) or from [The Qobuz Essential Discography](https://www.qobuz.com/us-en/awards/qobuz/discotheque_ideale/download-streaming-albums) if you use the ```--qobuz``` argument. The goal of the project is to help people find new music.

<!-- INSTALLATION -->
## Installation

The recommended method for installing ```albums-picker``` is [pipx](https://pipx.pypa.io/stable/):

```bash
pipx install albums-picker
```

To use with pip instead:

```bash
pip install albums-picker
```

<!-- USAGE EXAMPLES -->
## Usage

You can run ```albumsPicker``` just by typing:

```bash
albumsPicker
```

```bash
usage: albumsPicker [--qobuz] [--json] [--help]

Choose a random album from "1001 Albums You Must Hear Before You Die" or from "The Qobuz Essential Discography"

options:
  -h, --help   show this help message and exit
  -j, --json   Print album in json format
  -q, --qobuz  Choose an album from The Qobuz Essential Discography
```

<!-- LICENSE -->
## License

Distributed under the MIT License. See `LICENSE` for more information.

<!-- MARKDOWN LINKS & IMAGES -->
<!-- https://www.markdownguide.org/basic-syntax/#reference-style-links -->
[license-shield]: https://img.shields.io/github/license/santi224m/1001-albums-picker.svg?style=for-the-badge
[license-url]: https://github.com/santi224m/1001-albums-picker/blob/main/LICENSE
[terminal-screenshot]: https://raw.githubusercontent.com/santi224m/1001-albums-picker/main/images/screenshot_updated.png
<a href="https://www.flaticon.com/free-icons/vinyl" title="vinyl icons">Vinyl icons created by Freepik - Flaticon</a>
crop-yolo-v8 1.1.1
put yolov8m/n/x.pt in ~/.yolov8 and run
Requires Python: <4.0,>=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.
asediag 1.2.0
Aerosol Diagnostics on Model Native Grid
Requires Python: >=3.9

## Aerosol Diagnostics on Model Native Grid

**Aerosol process analysis in model-native Spectral Element (SE) grid**

Aerosol Diagnostics on Model Native Grid is a Python-based standalone tool designed for aerosol process analysis within the model-native spectral element (SE) grid. As such, it is also known as Aerosol SE Diagnostics or “asediag”. It includes functionalities for aerosol budget analysis, optical diagnostics, and decomposed anthropogenic aerosol radiative forcing analysis. The tool supports both SE and regular latitude-longitude (RLL) grids, and can operate in batch mode with multiprocessing capabilities.

**Primary Developer & Maintainer:** Taufiq Hassan

**For Questions:** [taufiq.hassan@pnnl.gov](taufiq.hassan@pnnl.gov)

[**Contributors**](./AUTHORS.rst)

Installation
------------

Git clone the repo
```bash
git clone git@github.com:TaufiqHassan/asediag.git
```

### Submit as a batch job

`asediag` works with e3sm_unifed environment in *Compy* and *Perlmutter*.

Make adjustments in the batch configuration file `batch_config.ini`.

Submit the batch jobs with:
```bash
python submit_asediag_batches.py
```
### Example 

[Demo output page](https://compy-dtn.pnl.gov/hass877/share/aer_diag/F20TR_2016Emis_ne30pg2_def_minus_F20TR_1850Emis_ne30pg2_def/aerosol.html)

Acknowledgement
---------------
`asediag` is based on earlier developments in `NCL` and individual contributions from:
* Po-Lun Ma: [Original budget analysis and diagnostics](https://github.com/eagles-project/aerosol_diag)
* Jian Sun & Kai Zhang: [Aerosol number and species per mode budget and spatial distribution analysis](https://github.com/eagles-project/aerosol_diag/tree/jian/develop)
* Jianfeng Li: [Aerosol optics diagnostics](https://github.com/eagles-project/aerosol_diag/tree/jianfeng_update)
* Kai Zhang: [Aerosol forcing diagnostics](https://github.com/kaizhangpnl/e3sm_erf_aerosol)

**Note:** Currently undegoing small patchworks and further developments!
mmmm 1.0.2
Google My Maps to MAPS.ME KML converter
Requires Python: >=3.0

Google My Maps to MAPS.ME KML converter
=======================================


This tool adapts KML files from Google My Maps
for use with [Organic Maps](https://organicmaps.app/) (and MAPS.ME),
striving to maintain color and icon accuracy.
Although Organic Maps supports fewer colors and icons,
the tool does its best to match the original as closely as possible.
Input on new icon mappings is appreciated.

Usage
-----

    mmmm google-maps.kml > maps-me.kml

Installation
------------

    pipx install mmmm

Development
-----------

You can create a virtual environment for testing by executing the commands below:

    micromamba env create --prefix ./.venv --file environment.yml
    micromamba activate ./.venv

Then, from the project root directory, you can run the converter with the following command:

    python -m mmmm google-maps.kml
OSMan1 0.1.6
The `OSMan1` library provides a set of utility functions for file and directory management tasks such as creating, removing, renaming, and listing files and directories. It also includes functions for file operations like reading and writing, as well as retrieving system and environmental information.
Requires Python: >=3.6

# OSMan1

`OSMan1` is a Python library for managing files and folders easily and quickly. The library includes multiple functions for creating, reading, writing, and deleting files and folders, as well as changing directories and listing directory contents.


## Usage

### Import the Library

```python
import OSMan1
```

### Available Functions

#### 1. Create a File

```python
OSMan1.touch('filename.txt')
```

#### 2. Read a File

```python
OSMan1.cat('filename.txt')
```

#### 3. Write to a File

```python
OSMan1.nano('filename.txt', 'w', 'Hello, World!')
```

#### 4. Delete a File

```python
OSMan1.rm('filename.txt')
```

#### 5. Create a Folder

```python
OSMan1.mkdir('foldername')
```

#### 6. Delete a Folder

```python
OSMan1.rmdir('foldername')
```

#### 7. Rename a File or Folder

```python
OSMan1.ren('oldname', 'newname')
```

#### 8. Move a File or Folder

```python
OSMan1.mv('source_path', 'destination_path')
```

#### 9. Change the Current Directory

```python
OSMan1.cd('path')
```

#### 10. Display Directory Tree

```python
OSMan1.tree('path')
```

#### 11. Display Current Directory

```python
OSMan1.pwd()
```

#### 12. List Directory Contents

```python
OSMan1.ls('path')
```

#### 13. List Directory Contents with Details

```python
OSMan1.ls_l('path')
```

#### 14. Display Last Access Time of a File

```python
OSMan1.istime('filename.txt')
```

#### 15. Display File Size

```python
OSMan1.size('filename.txt')
```

#### 16. Display System Information

```python
OSMan1.get_info()
```

#### 17. Display Executable Paths

```python
OSMan1.get_path()
```

---

These sections provide clear instructions on how to install and use the `OSMan1` library with examples of each function it offers.