semvec/pypi-packages huggingface dataset:
petram-rfp 26.2.10
Plasma wave physics for RF module
Requires Python: >=3.10
Keywords: scientific computing, waves in plasmas

## Petra-M(RFP)

This module provides additional module for waves in plasmas

### EM3D : Frequency domain Maxwell equation in 3D
  Domain:   
 *    EM3D_ColdPlasma  : Cold magnetised plasma
 *    EM3D_LkPlasma    : local-K approx. plasma
 
### EM2Da : Frequency domain Maxwell equation in 2D axissymetric space
  Domain:   
 *    EM2Da_ColdPlasma  : Cold magnetised plasma
 *    EM2Da_LkPlasma    : local-K approx. plasma

### EM2D : Frequency domain Maxwell equation in 2D space
  Domain:   
 *    EM2D_ColdPlasma  : Cold magnetised plasma
 *    EM2D_LkPlasma    : local-K approx. plasma

### EM1D : Frequency domain Maxwell equation in 1D
  Domain:   
 *    EM1D_ColdPlasma  : Cold magnetised plasma
 *    EM1D_LkPlasma    : local-K approx. plasma
nexus-trust-exchange 0.1.0
Agent Trust Exchange - viral registry and communication board for AI agents (RESEARCH PROTOTYPE)
Requires Python: >=3.9
Keywords: agents, ai, exchange, registry, trust

# Nexus Trust Exchange

**Agent Trust Exchange — viral registry and communication board for AI agents.**

> ⚠️ **RESEARCH PROTOTYPE** — This module is in pre-alpha. Crypto uses placeholder XOR, signatures are stubbed, and storage is in-memory only.

## Overview

Nexus provides a decentralized trust exchange layer for AI agent ecosystems. It enables agents to:

- **Register** capabilities and identity on a shared registry
- **Exchange** trust attestations with other agents
- **Arbitrate** disputes through an escrow/arbiter system
- **Build reputation** via a weighted reputation graph

## Installation

```bash
pip install nexus-trust-exchange
```

## Components

| Module | Purpose |
|--------|---------|
| `registry.py` | Agent registration and capability discovery |
| `client.py` | Client SDK for interacting with the exchange |
| `arbiter.py` | Trust dispute resolution |
| `escrow.py` | Conditional trust escrow |
| `dmz.py` | Demilitarized zone for untrusted agent interaction |
| `reputation.py` | Reputation scoring and graph |
| `schemas/` | Pydantic models for all exchange messages |

## Quick Start

```python
from nexus import NexusRegistry, NexusClient

# Create a registry
registry = NexusRegistry()

# Register an agent
registry.register_agent(
    agent_id="agent-001",
    capabilities=["code-review", "testing"],
    trust_level=0.8
)
```

## Part of Agent-OS

This module is part of the [Agent-OS](https://github.com/imran-siddique/agent-os) ecosystem. Install the full stack:

```bash
pip install agent-os-kernel
```

## License

MIT
pypomp 0.4.3
Modeling and inference using partially observed Markov process (POMP) models
Requires Python: >=3.10

[![Project Status: Active – The project has reached a stable, usable state and is being actively developed.](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active)
[![codecov](https://codecov.io/gh/pypomp/pypomp/graph/badge.svg?token=8TA2X3DRML)](https://codecov.io/gh/pypomp/pypomp)

# pypomp

Python code for modeling and inference using partially observed Markov process (POMP) models.
See the [tutorials](https://pypomp.github.io/tutorials) for user-friendly guides, and the [quantitative tests](https://pypomp.github.io/quant) for additional technical examples.

### Expected package users

* Scientists wanting to perform data analysis on a dynamic system via partially observed Markov processes (POMPs), also called state-space models (SSM) or hidden Markov models (HMM) in other contexts.

  * This package design and intended use is similar to the popular [**pomp** R package](https://kingaa.github.io/pomp/). As such, many of the expected use cases and motivating examples of this package can be found on the [pomp package bibliography page](https://kingaa.github.io/pomp/biblio.html).
  
* Researchers wishing to develop novel inference methodology for POMP models.

  * Like the **pomp** R package, this package provides a framework for implementing computer representations of arbitrary POMP models. This ability provides an environment for researchers to develop, test, and deploy novel algorithms that are applicable to POMP models.
 
### Key features 

* Estimation, filtering, and inference for highly nonlinear, non-Gaussian state space models via the particle filter.

* New algorithms for model-fitting. Gradient descent using a new [gradient estimate](https://arxiv.org/abs/2407.03085) initialized with a [warm-start](https://www.pnas.org/doi/full/10.1073/pnas.1410597112) allows for improved maximum-likelihood inference in even highly challenging epidemiological models, while the gradient estimate can readily be plugged into a sampler from [Tensorflow Probability](https://www.tensorflow.org/probability/api_docs/python/tfp/mcmc/NoUTurnSampler) to facilitate more efficient Bayesian inference. 

* This package leverages JAX for GPU support and just-in-time compilation, enabling a speedup of up to 16x when compared to the **pomp** R package.

### Package Development 

* The **pypomp** package is currently in early and active development. Backward compatibility is not yet a major consideration. Tutorials and quantitative tests may not all run on the latest pypomp version.

* All contributions are welcome! Contributions should keep in mind the intended uses of this package, and its intended users.

* The **pypomp** package is run by the [pypomp organization](https://github.com/pypomp).