semvec/pypi-packages huggingface dataset:
networking-baremetal 6.4.1
Neutron plugin that provides deep Ironic/Neutron integration.
Requires Python: >=3.8

networking-baremetal plugin
---------------------------

This project's goal is to provide deep integration between the Networking
service and the Bare Metal service and advanced networking features like
notifications of port status changes and routed networks support in clouds
with Bare Metal service.

* Free software: Apache license
* Documentation: http://docs.openstack.org/networking-baremetal/latest
* Source: http://opendev.org/openstack/networking-baremetal
* Bugs: https://bugs.launchpad.net/networking-baremetal
* Release notes: https://docs.openstack.org/releasenotes/networking-baremetal/
folderfirewall 5.0.0
Security-focused sandbox for untrusted folders
Requires Python: >=3.11

# FolderFirewall 🔒📂

FolderFirewall is a cross-platform security application that disguises itself as a folder.  
When opened, it launches a **cloned, sandboxed system environment** where all activity stays encrypted, isolated, and secure.  

## ✨ Features
- 🗂️ Appears as a normal folder on the desktop
- 🔑 Asks for access before launching
- 🖥️ Creates a **cloned secure system** or opens a saved one
- 🔒 Encrypted overlay filesystem (AES-256)
- 🧹 Option to **save, discard, or snapshot** after each session
- 🌐 Configurable network rules (offline / VPN / Tor / full)
- 📝 Transparent logs for auditing

## 🚀 Roadmap
- [ ] Prototype (Linux) using LUKS + Podman sandbox
- [ ] Electron UI for launching clones
- [ ] Windows VHD + BitLocker integration
- [ ] macOS encrypted DMG + sandbox
- [ ] Export/import encrypted clones
- [ ] CI/CD with GitHub Actions

## 📂 Repository Structure
osmodi-solver 0.1.1
GPU-accelerated Poisson solver for pressure from PIV
Requires Python: >=3.10
Keywords: pressure-from-piv particle-image-velocimetry PIV poisson poisson-solver gpu fluid-dynamics solver

**OSMODI** is a GPU-accelerated Poisson solver for pressure reconstruction from
Particle Image Velocimetry (PIV) data. It provides a CUDA implementation for NVIDIA GPUs
with a reliable CPU fallback.

## Github Repository

[Link](https://github.com/3dfernando/pressure-osmosis)
---

## Features

- CUDA GPU solver (NVIDIA GPUs)
- CPU-only fallback (always available)
- 2D and 3D structured grids
- Float32 and Float64 support
- Designed for pressure-from-PIV workflows

---

## Installation

```bash
pip install osmodi-solver
```

---

## Quick Start
```python 
import osmodi
import numpy as np

print("GPU available:", osmodi.GPU_AVAILABLE)
print("CPU available:", osmodi.CPU_AVAILABLE)

# Create sample velocity gradients
Sx = np.random.randn(64, 64, 64).astype(np.float32)
Sy = np.random.randn(64, 64, 64).astype(np.float32)
Sz = np.random.randn(64, 64, 64).astype(np.float32)

delta = np.array([1.0])

if osmodi.GPU_AVAILABLE:
    P, progress = osmodi.solve_gpu(Sx, Sy, Sz, delta)
else:
    P, progress = osmodi.solve_cpu(Sx, Sy, Sz, delta)

print("Output shape:", P.shape)
```

---

## API Overview

solve_cpu(Sx, Sy, Sz, delta, options=None)
Runs the CPU-based Poisson solver.

solve_gpu(Sx, Sy, Sz, delta, options=None)
Runs the CUDA GPU-based Poisson solver.

### Parameters
Sx, Sy, Sz: NumPy arrays (2D or 3D) of velocity gradients

delta: grid spacing NumPy array

options: optional dictionary for solver configuration

### Returns
P: pressure field (same shape as input)

progress: convergence history (NumPy array)

### Solver Options
```python
options = {
    "Verbose": True,               # Print solver progress
    "SolverToleranceRel": 1e-4,   # Relative tolerance
    "SolverToleranceAbs": 1e-6,   # Absolute tolerance
    "Kernel": "face-crossing",     # or 'cell-centered'
}
```

---

## Supported Hardware
CPU: x86-64, any modern processor

GPU (optional): NVIDIA Pascal or newer 

RAM: 4 GB minimum, 16+ GB recommended for large grids

---

## Citation
If you use this solver in your work, please cite:

Zigunov, F., & Charonko, J. J. (2024). One-shot omnidirectional pressure integration through matrix-free multiscale methods. Measurement Science and Technology.

---

## License
GNU General Public License v3.0 (GPL-3.0)
ironic 26.1.5
OpenStack Bare Metal Provisioning
Requires Python: >=3.8

======
Ironic
======

Team and repository tags
------------------------

.. image:: https://governance.openstack.org/tc/badges/ironic.svg
    :target: https://governance.openstack.org/tc/reference/tags/index.html

Overview
--------

Ironic consists of an API and plug-ins for managing and provisioning
physical machines in a security-aware and fault-tolerant manner. It can be
used with nova as a hypervisor driver, or standalone service using bifrost.
By default, it will use PXE and IPMI to interact with bare metal machines.
Ironic also supports vendor-specific plug-ins which may implement additional
functionality.

Ironic is distributed under the terms of the Apache License, Version 2.0. The
full terms and conditions of this license are detailed in the LICENSE file.

Project resources
~~~~~~~~~~~~~~~~~

* Documentation: https://docs.openstack.org/ironic/latest
* Source: https://opendev.org/openstack/ironic
* Bugs: https://bugs.launchpad.net/ironic/+bugs
* Wiki: https://wiki.openstack.org/wiki/Ironic
* APIs: https://docs.openstack.org/api-ref/baremetal/index.html
* Release Notes: https://docs.openstack.org/releasenotes/ironic/
* Design Specifications: https://specs.openstack.org/openstack/ironic-specs/

Project status, bugs, and requests for feature enhancements (RFEs) are tracked
in Launchpad:
https://launchpad.net/ironic

For information on how to contribute to ironic, see
https://docs.openstack.org/ironic/latest/contributor
pyweatherfiles 0.0.0
A generator for Typical Meteorological Year (TMY) files and weather data management.
Requires Python: >=3.7

# pyweatherfiles

A Python package to manage weather files and generate Typical Meteorological Year (TMY) data from historical records.

## Installation

```bash
pip install pyweatherfiles
```

## Usage

```python
from pyweatherfiles import tmy

# See examples/using_tmy_generator.py for a complete example.
generator = tmy.TMYGenerator("path/to/your/weather_data.csv")
# ... check the example file for more details
```

## Description
This tool allows you to:
- Load weather data from CSV or Excel.
- Calculate CDFs using daily or hourly methods.
- Apply weighting statistics (Sandia or TMY3).
- Select candidate months based on Finkelstein-Schafer statistics.
- Apply persistence criteria.
- Smooth the data at monthly junctions.
- Generate a final TMY file.

## License
MIT
kyutil 0.3.18
Python utils for kylin python application
Requires Python: >=3.11

# kyutil
麒麟python工具库

    Changelog:
        ### 0.3.2
            - FEATURE: 增加EPKL仓库比对
        ### 0.2.16
            - FEATURE: koji客户端增加获取继承tag列表
        ### 0.2.13
            - FIX: pungi-koji集成workdir设置 BUG修复
        ### 0.2.12
            - FEATURE: 仓库比对BUG修复
        ### 0.2.11
            - FEATURE: pungi-koji增加构建类型
        ### 0.2.10
            - FEATURE: 调用jenkins执行任务后，增加执行状态返回值
        ### 0.2.9
            - FIX: 修改集成容器任务为环境变量读取，修改image_tag为外部传入
        ### 0.2.5
            - FIX: 自检报告的文件名称 从 自检报告 改为 self-check-report
        ### 0.2.4
            - FEATURE: Compose制品回传增加os目录回传（除了Packages）    
        ### 0.1.12
            - BUGFIX: 修复在mock环境内找不到celery.log的BUG    
        ### 0.1.0
            - initial release
yaafpy 0.1.2
Yet Another Agentic Framework
Requires Python: >=3.9

# YAAF (Yet Another Agentic Framework)

A stream-first, middleware-based Python framework for building agentic AI flows.

## Features
- **Flexible**: You can use any LLM, tools, and storage.
- **Functional Programming Approach**: Use composable and reusable middleware to build a workflow.
- **Stream Architecture Available**: Also Built for streaming pipelines.
- **Nested Workflows**: Compose complex behaviors by nesting workflows.
- **Type Safe**: Built with Pydantic for validation and serialization.

## Installation
```bash
pip install yaafpy
```

## Quick Start
```python
import asyncio
from yaafpy.types import AgentConfig, ExecContext
from yaafpy.workflows import Workflow

async def echo_step(ctx):
    ctx.input = f"Echo: {ctx.input}"
    return ctx

async def main():
    wf = Workflow()
    wf.use(echo_step)    
    ctx = ExecContext(session_id="test", agent=AgentConfig(model="gpt-4"), input="Hello")
    result = await wf.run(ctx)
    print(result.input)

if __name__ == "__main__":
    asyncio.run(main())
```