semvec/pypi-packages huggingface dataset:
PyOdoo 0.7.2
API for Odoo

# PyOdoo

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**Description:** API for Odoo

**Copyright:** 2021-2025 Fabio Castelli (Muflone) <muflone@muflone.com>

**License:** GPL-3+

**Source code:** https://github.com/muflone/pyodoo

**Documentation:** http://www.muflone.com/pyodoo/

# Description

PyOdoo is an attempt to build a common API to interact with any
[**Odoo**](https://www.odoo.com/) server in order to operate using
its XML RPC API.

# System Requirements

* Python 3.x
* xlrd 2.0.x (https://pypi.org/project/xlrd/)
* requests 2.32.x (https://pypi.org/project/requests/)

# Usage

Please see the **samples** and the **tests** folders for some usage examples.
antishlopcli 1.1.8
AI-Powered Security Vulnerability Scanner with Enhanced UI
Requires Python: >=3.8

# AntishlopCLI

AI-powered security vulnerability scanner for source code.

## Installation

```bash
pip install antishlopcli
```

## Quick Start

1. Set your OpenAI API key:
```bash
antishlop --set-key sk-your-openai-api-key-here
```

2. Scan your code:
```bash
antishlop /path/to/your/code
```

## Features

- Pattern detection for common vulnerabilities
- AI-powered context analysis 
- OWASP/CWE compliance mapping
- JSON output for automation

## Usage

```bash
# Scan current directory
antishlop .

# JSON output
antishlop --output json ./src
```

## Requirements

- Python 3.8+
- OpenAI API key

## Disclaimer

This tool is provided "AS IS" for educational and defensive security purposes only. The author is not liable for any damages, legal issues, or consequences arising from its use. Users are solely responsible for ensuring they have proper authorization before scanning any codebase and for complying with all applicable laws and regulations.

## License

MIT License - see LICENSE file for details.
ultrasound-metrics 0.1.0
Metrics for ultrasound data and images
Requires Python: <4.0,>=3.10
Keywords: python

# ultrasound-metrics

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> **⚠️ Alpha Release**
>
> This library is currently under active development and is released as an alpha version. The primary goal of this release is to collect community feedback.

## Introduction

*ultrasound-metrics* is an open-source Python library for ultrasound data and image quality analysis developed at [Forest Neurotech](https://forestneurotech.org/). It is written in the [Array API standard](https://data-apis.org/array-api/2024.12/), making it compatible across NumPy, JAX, and PyTorch backends. The implementation supports GPU acceleration (CuPy, JAX, PyTorch), software acceleration (JIT or AOT compilation), and interactive region-of-interest selection for metrics in 2D.

Documentation on *ultrasound-metrics* can be found [here](https://forest-neurotech-ultrasound-metrics.readthedocs-hosted.com/latest/) and examples can be viewed [here](https://forest-neurotech-ultrasound-metrics.readthedocs-hosted.com/latest/example_gallery/index.html). We are actively taking [requests](https://github.com/Forest-Neurotech/ultrasound-metrics/issues) for additional metrics that may be helpful to ultrasound researchers.

## Installation

### Install from PyPi (recommended:)

```bash
pip install ultrasound-metrics
```

### Build from source

```bash
make install
```

Build prerequisites:
* `uv >= 0.6.10`
* optional: `make`

## Documentation
We currently support the following ultrasound data and image quality metrics:
* contrast-to-noise ratio (CNR)
* generalized contrast-to-noise ratio (gCNR)
* signal-to-noise ratio for raw radiofrequency signals (RF SNR)
* temporal signal-to-noise ratio (tSNR)
* sharpness (tenengrad)
* coherence factor
and more!

We are actively taking requests for metrics, ultrasound data file types to support, and additional features that would be helpful to the ultrasound imaging community. To make a feature request, please [submit a GitHub issue](https://github.com/Forest-Neurotech/ultrasound-metrics/issues).

## Acknowledgements

ultrasound-metrics builds upon the excellent work of the ultrasound imaging community:

- **[ultraspy](https://ultraspy.readthedocs.io/en/latest/index.html#)** - For educational examples and validation benchmarks
- **[PICMUS](https://www.creatis.insa-lyon.fr/Challenge/IEEE_IUS_2016/)** - For public, standardized datasets used in examples

This package was developed by the [Forest Neurotech](https://forestneurotech.org/) team, a [Focused Research Organization](https://www.convergentresearch.org/about-fros) supported by [Convergent Research](https://www.convergentresearch.org/) and [generous philanthropic funders](https://www.convergentresearch.org/fro-portfolio).