semvec/pypi-packages huggingface dataset:
coelo 0.0.1
High-performance scheduling infrastructure for B2B.

# Coelo

High-performance scheduling infrastructure built for speed and scalability.

**Note:** This package is currently under active development. 

- **Website:** [getcoelo.com.br](https://getcoelo.com.br)
- **X (Twitter):** [@getcoelo](https://x.com/getcoelo)
- **GitHub:** [getcoelo/coelo](https://github.com/getcoelo/coelo)
rohith 0.1.4
A terminal-based CLI card to showcase Rohith Gowda's profile and quick contact tools.

# 📦 rohith (CLI Tool) [![PyPI version](https://badge.fury.io/py/rohith.svg)](https://pypi.org/project/rohith/)


rohith is a simple and elegant command-line portfolio card. It lets you display your professional bio, links to your social profiles, and options like downloading your resume, contacting you via Formspree, or scheduling a meeting — all directly from the terminal.

This tool is great for sharing your portfolio in a unique and tech-friendly way.

🚀 Features
View a professional CLI portfolio card

Quick access to:
- Email contact form
- Resume download
- Meeting scheduler

Powered by Rich and Questionary for an interactive and visually appealing experience 

---
```
cli-card/
├── rohith/                  
│   ├── __init__.py          
│   └── cli.py               # main logic
├── README.md                
├── LICENSE                  
├── pyproject.toml           # Python build config
├── setup.cfg                # Project metadata and dependencies
├── setup.py (optional)
```
---
## 📦 Published on PyPI

[![PyPI version](https://badge.fury.io/py/rohith.svg)](https://pypi.org/project/rohith/)

This package is published on [PyPI](https://pypi.org/project/rohith/)

---
## Installation
You can run this CLI tool instantly without installing anything globally:
```
pipx run rohith
```

Make sure you have pipx installed. If not, you can install it with:
```
python3 -m pip install --user pipx
python3 -m pipx ensurepath
```

Alternatively, you can install it permanently:
```
pipx install rohith
```
Then simply run:
```
rohith
```
---

> **Important Notice:**  
> This project is licensed under the [MIT License](LICENSE).  
> According to the license terms, any redistribution (including compiled or modified versions), you **must** retain the original copyright
> notice and the full license text. Copyright © 2025 Rohith Gowda R. All rights reserved.
sal-xarray 0.2.1
SAL backend for xarray
Requires Python: <3.13,>=3.9

# sal-xarray

An xarray backend for SAL (Simple Access Layer) that enables seamless integration of SAL data sources with the xarray ecosystem.

## Overview

sal-xarray provides a backend plugin for xarray that allows you to access SAL data sources using xarray's familiar API. It automatically handles the conversion of SAL signals to xarray DataArrays and Datasets, making it easy to work with SAL data in scientific Python workflows.
## Features

- **xarray Integration**: Access SAL data using xarray's `open_dataset` function
- **Automatic Conversion**: Converts SAL signals to xarray DataArrays with proper coordinates and metadata
- **Status Mask Handling**: Includes status mask data alongside signal data
- **URI-based Access**: Simple URI scheme (`sal://pulse/<shot_number>/<signal_name>`) for accessing data

## Installation 

```bash
pip install sal-xarray
```

Or using `uv`:

```bash
uv pip install sal-xarray
```

JET data access requires a VPN connection to the JDC. You will also require a config file at `~/.sal/credentials` with the following content:

```toml
[https://sal.jetdata.eu]
user=<user-name>
password=<password>
```

## Usage

Open a SAL dataset using xarray:

```python
import xarray as xr

# Open a SAL signal by name and shot number
ds = xr.open_dataset("sal://pulse/87737/ppf/signal/jetppf/magn/ipla", engine="sal")

# Access the data
data = ds["data"]
status = ds["status"]

# The dataset includes time coordinates
time = ds["time"]

# Access metadata
units = ds["data"].attrs["units"]
signal_name = ds["data"].attrs["sal_name"]
```

The URI format is: `sal://pulse/<shot_number>/<signal_name>`

## Data Structure

When you open a SAL dataset, sal-xarray creates an xarray Dataset with:
- **data**: The signal data as a DataArray
- **status**: The status data as a DataArray
- **time**: Time coordinates (dimension)
- **attrs**: Metadata including units and SAL signal name

## Development

### Setup

Clone the repository and install development dependencies:

```bash
git clone https://github.com/samueljackson92/sal-xarray.git
cd sal-xarray
uv sync
```

### Running Tests

```bash
uv run pytest tests/
```

### Code Quality

The project uses ruff for linting and formatting, and pylint for additional checks:

```bash
# Run ruff
uv run ruff check sal_xarray tests
uv run ruff format sal_xarray tests

# Run pylint
uv run pylint sal_xarray

# Run ty
uv run ty check sal_xarray
```

## Contributing

Contributions are welcome! Please ensure:

1. Tests pass for any new functionality
2. Code follows the project's style guidelines (ruff and pylint)
3. Documentation is updated as needed

## License

MIT License
uda-xarray 0.2.1
UDA backend for xarray
Requires Python: <3.13,>=3.9

# uda-xarray

An xarray backend for UDA (Universal Data Access) that enables seamless integration of UDA data sources with the xarray ecosystem.

## Overview

uda-xarray provides a backend plugin for xarray that allows you to access UDA data sources using xarray's familiar API. It automatically handles the conversion of UDA signals to xarray DataArrays and Datasets, making it easy to work with UDA data in scientific Python workflows.

## Features

- **xarray Integration**: Access UDA data using xarray's `open_dataset` function
- **Automatic Conversion**: Converts UDA signals to xarray DataArrays with proper coordinates and metadata
- **Error Handling**: Includes error data alongside signal data
- **URI-based Access**: Simple URI scheme (`uda://signal_name:shot`) for accessing data

## Installation 

```bash
pip install uda-xarray
```

Or using `uv`:

```bash
uv pip install uda-xarray
```

## Usage

Open a UDA dataset using xarray:

```python
import xarray as xr

# Open a UDA signal by name and shot number
ds = xr.open_dataset("uda://ip:30421", engine="uda")

# Access the data
data = ds["data"]
errors = ds["error"]

# The dataset includes time coordinates
time = ds["time"]

# Access metadata
units = ds["data"].attrs["units"]
signal_name = ds["data"].attrs["uda_name"]
```

The URI format is: `uda://<signal_name>:<shot_number>`

## Data Structure

When you open a UDA dataset, uda-xarray creates an xarray Dataset with:

- **data**: The signal data as a DataArray
- **error**: The error data as a DataArray
- **time**: Time coordinates (dimension)
- **attrs**: Metadata including units and UDA signal name

## Requirements

- Python >= 3.11, < 3.13
- uda >= 2.9.2
- xarray >= 2025.12.0

## Development

### Setup

Clone the repository and install development dependencies:

```bash
git clone https://github.com/samueljackson92/uda-xarray.git
cd uda-xarray
uv sync
```

### Running Tests

```bash
pytest tests/
```

### Code Quality

The project uses ruff for linting and formatting, and pylint for additional checks:

```bash
# Run ruff
uv run ruff check uda_xarray tests
uv run ruff format uda_xarray tests

# Run pylint
uv run pylint uda_xarray
```

## Contributing

Contributions are welcome! Please ensure:

1. Tests pass for any new functionality
2. Code follows the project's style guidelines (ruff and pylint)
3. Documentation is updated as needed

## License

MIT License