semvec/pypi-packages huggingface dataset:
hgbest-client-python 0.1.5
Python SDK for the HgBest API, providing secure, signed access to various services.
Requires Python: >=3.6
Keywords: hgbest api sdk client

# HgBest Python SDK

    This is the official Python SDK for the HgBest API. It allows developers to easily integrate with the HgBest platform to access various services, including risk analysis, health reports, and financial insights.

    ## Installation
nekro-agent-toolkit 1.6.1
Nekro Agent 安装、更新与备份的统一管理工具
Requires Python: >=3.6

# Nekro Agent Toolkit

<p align="center">
	<img src="https://cdn.jsdelivr.net/gh/greenhandzdl/nekro-agent-toolkit@main/icons/nekro-agent-toolkit-icons.png" alt="Nekro Agent Toolkit 吉祥物">
</p>

Nekro Agent Toolkit 是一款用于一站式部署、备份、恢复 Nekro Agent 及相关服务的工具，支持 Docker 环境下的自动化管理。

## 🌐 其他语言用户请参考

| [Read in English](https://cdn.jsdelivr.net/gh/greenhandzdl/nekro-agent-toolkit@main/doc/README/README-EN.md) | [اقرأ باللغة العربية](https://cdn.jsdelivr.net/gh/greenhandzdl/nekro-agent-toolkit@main/doc/README/README-AR.md) | [Lire en français](https://cdn.jsdelivr.net/gh/greenhandzdl/nekro-agent-toolkit@main/doc/README/README-FR.md) | [Читать на русском](https://cdn.jsdelivr.net/gh/greenhandzdl/nekro-agent-toolkit@main/doc/README/README-RU.md) | [Leer en español](https://cdn.jsdelivr.net/gh/greenhandzdl/nekro-agent-toolkit@main/doc/README/README-ES.md) | [日本語で読む](https://cdn.jsdelivr.net/gh/greenhandzdl/nekro-agent-toolkit@main/doc/README/README-JP.md) |

## ✨ 主要功能

- 一键安装、升级、备份与恢复 Nekro Agent
- 智能检测与多语言支持
- 支持 Docker 卷自动备份与恢复

## 🚀 快速使用

### 安装

```bash
pip install nekro-agent-toolkit
# 或源码运行
git clone https://github.com/your-repo/nekro-agent-toolkit.git
cd nekro-agent-toolkit
```

### 常用命令

```bash
# 安装/升级/备份/恢复
nekro-agent-toolkit -i [PATH]
nekro-agent-toolkit -u [PATH]
nekro-agent-toolkit -b [DATA_DIR] BACKUP_DIR
nekro-agent-toolkit -r BACKUP_FILE [DATA_DIR]
```

### 使用 uv 管理依赖（推荐）

本项目现在支持使用 `uv` 来管理依赖与生成可重现的锁文件 `uv.lock`。

## 附加信息

- 系统要求：Python 3.6+，Docker，Docker Compose
- 许可证：见 [LICENSE](https://cdn.jsdelivr.net/gh/greenhandzdl/nekro-agent-toolkit@main/LICENSE)
geoh5py 0.12.0
Python API for geoh5, an open file format for geoscientific data
Requires Python: <4.0,>=3.10
Keywords: geology, geophysics, data, interoperability

geoh5py: Python API for geoh5. An open file format for geoscientific data
==========================================================================

The **geoh5py** library has been created for the manipulation and storage of a wide range of
geoscientific data (points, curve, surface, 2D and 3D grids) in
`geoh5 file format <https://gist.github.com/jincandescent/06a3bd4e0e54360ad191>`_.
Users will be able to directly leverage the powerful visualization
capabilities of `Geoscience ANALYST <https://mirageoscience.com/mining-industry-software/geoscience-analyst/>`_ along with open-source code from the Python ecosystem.

Installation
^^^^^^^^^^^^
**geoh5py** depends on `NumPy <https://numpy.org/>`_ and `h5py <https://www.h5py.org/>`_.

.. note:: Users will likely want to take advantage of other packages available in the Python ecosystem.
    We therefore recommend using `Miniforge <https://github.com/conda-forge/miniforge#download>`_ `(Windows x86_64) <https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-Windows-x86_64.exe>`_


Install **geoh5py** from PyPI::

    $ pip install geoh5py


Feedback
^^^^^^^^
Have comments or suggestions? Submit feedback.
All the content can be found on the github_ repository.

.. _github: https://github.com/MiraGeoscience/geoh5py


Visit `Mira Geoscience website <https://mirageoscience.com/>`_ to learn more about our products
and services.


License
^^^^^^^
geoh5py is free software: you can redistribute it and/or modify
it under the terms of the GNU Lesser General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.

geoh5py is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU Lesser General Public License for more details.

You should have received a copy of the GNU Lesser General Public License
along with geoh5py.  If not, see <https://www.gnu.org/licenses/>.


Copyright
^^^^^^^^^
Copyright (c) 2025 Mira Geoscience Ltd.
swh.coarnotify 0.8.0
Software Heritage COAR Notify server
Requires Python: >=3.11

Software Heritage - COAR Notify
===============================

A `COAR Notify`_ server implementation in Django. It provides a receiving endpoint
(Inbox) and handlers for `Announce Relationship pattern`_.

It is currently experimental and **not** suited for production.

.. _COAR Notify: https://coar-notify.net
.. _Announce Relationship pattern: https://coar-notify.net/specification/1.0.1/announce-relationship/
localstack-extension-typedb 0.1.2
LocalStack Extension: TypeDB on LocalStack
Requires Python: >=3.9
Keywords: LocalStack, TypeDB

TypeDB on LocalStack
=====================

This repo contains a [LocalStack Extension](https://github.com/localstack/localstack-extensions) that facilitates developing [TypeDB](https://typedb.com)-based applications locally.

After installing the extension, a TypeDB server instance will become available under `typedb.localhost.localstack.cloud:4566`, allowing you to create and manage TypeDB databases directly from your AWS applications running in LocalStack.

For example, you could create a microservice backed by a Lambda function that connects to a TypeDB database upon invocation. See [here](https://github.com/typedb-osi/typedb-localstack-demo) for a simple example application that makes use of this extension.

## Configuration

The following environment variables can be passed to the LocalStack container (make sure to prefix them with `LOCALSTACK_...` when using the `localstack start` CLI), to steer the behavior of the extension:

* `TYPEDB_FLAGS`: Additional user-defined command args to pass to the TypeDB container.
* `TYPEDB_HTTP2_PROXY`: Flag to enable/disable HTTP2 proxy for gRPC traffic (use this if you experience network issues, and use the HTTP variant of the TypeDB driver).

## Prerequisites

* Docker
* LocalStack Pro (free trial available)
* `localstack` CLI
* `make`

## Install from GitHub repository

This extension can be installed directly from this Github repo via:

```bash
localstack extensions install "git+https://github.com/localstack/localstack-extensions.git#egg=typedb&subdirectory=typedb"
```

## Install local development version

Please refer to the docs [here](https://github.com/localstack/localstack-extensions?tab=readme-ov-file#start-localstack-with-the-extension) for instructions on how to start the extension in developer mode.

## Change Log

* `0.1.1`: Minor fixes in CI setup, exception handling
* `0.1.0`: Initial version of the extension

## License

The code in this repo is available under the Apache 2.0 license.
kamcp 0.1.5
mcp server for kam.
Requires Python: >=3.12

# Kamcp
kam mcp
# Install
- pip install kamcp

- uv tool install kamcp

- uv pip install kamcp (no x)

# config 
- Supports all startup methods.
- Use `kamcp --help` to view help information.
```json
{
...
"command":"kamcp",
"args":["stdio"]
...
}
```
# This is a sub-project of the kam project.
hyvitasbench 1.0.0
Official implementation of HyViTas-Bench, from 'A CNN-ViT hybrid architecture search benchmark on a large-scale dataset' (IEEE Access 2025)
Requires Python: >=3.9
Keywords: NAS, Benchmark, Dataset, DeepLearning

# HyViTas-Bench
Code release for **[A CNN-ViT hybrid architecture search benchmark on a large-scale dataset](https://doi.org/10.1109/ACCESS.2025.3642734)**

## Getting started

### Installation:
```bash
pip3 install hyvitasbench
```

### Usage:
```python
import torch
from hyvitasbench import *
import hyvitasbench.datasets as datasets

bench = HyViTASBench('hyvitasbench.pkl.zst')
exemplar = bench[1000]

print(exemplar.genotype)
# [[[32, 2, 3], [32, 2, 3], [32, 2, 3]], [[64, 2, 3], [64, 2, 3], [64, 2, 3]], [[112, 8, 1, 5], [112, 8, 1, 5], [112, 8, 1, 5]], [[224, 8, 1, 5], [224, 8, 1, 5], [224, 8, 1, 5]]]

# Available modes: mean, random, normal, trunc_normal, raw
print(exemplar.evaluate(mode='mean'))
# 67.164

print(exemplar.evaluate(mode='mean', dataset=datasets.OOD_RED_IMAGENET_SKETCH))
# 6.784834669601216

for dev_id, dev_name in bench.device_names.items():
    print(f"{dev_id:20s} : {dev_name}")
# aarch64_rpizero2w    : Raspberry Pi Zero 2W
# aarch64_rpi4         : Raspberry Pi 4
# tegra_orin-nano      : Jetson Orin Nano
# ...

print(exemplar.get_inference_time('tegra_orin-nano'))
# 19.04989266

net = exemplar.instantiate(device=torch.device('cuda'))  # Instantiate PyTorch model
print(net)
# Network(
# ...
# )

other_exemplar = bench.lookup([
    [[48, 4, 3], [48, 4, 3], [48, 4, 3]],
    [[64, 4, 3], [64, 4, 3], [64, 4, 3]],
    [[144, 8, 4, 5], [144, 8, 4, 5], [144, 8, 4, 5]],
    [[256, 8, 4, 5], [256, 8, 4, 5], [256, 8, 4, 5]]
])
print(other_exemplar.index)
# 1439
```

## Download
The benchmark data archive can be downloaded from the repository release files.

The RedImageNet dataset can be downloaded from https://dx.doi.org/10.21227/egmb-rc53.

## Contributors
- Luca Robbiano <luca.robbiano@polito.it>

## Citation
```
@article{hyvitasbench,
    title        = {A CNN-ViT hybrid architecture search benchmark on a large-scale dataset},
    author       = {Robbiano, Luca and Pistilli, Francesca and Averta, Giuseppe},
    year         = {2025},
    journal      = {IEEE Access},
    volume       = {13},
    pages        = {209965-209979}
    doi          = {10.1109/ACCESS.2025.3642734}
    number       = {}
}
```

## License
This codebase is released under the MIT License, except for portions of files that explicitly state otherwise.
Please check in-file notices for any exceptions.
inductiva 0.18.12
Python client for the Inductiva API
Requires Python: >=3.9


[![Python package](https://github.com/inductiva/inductiva/actions/workflows/python-package.yml/badge.svg)](https://github.com/inductiva/inductiva/actions/workflows/python-package.yml) [![Python version](https://img.shields.io/pypi/pyversions/inductiva.svg)](https://www.python.org/downloads/) [![Documentation](https://img.shields.io/badge/Documentation-blue.svg)](https://docs.inductiva.ai/en/latest/) [![Benchmarks](https://img.shields.io/badge/Benchmarks-blue.svg)](https://benchmarks.inductiva.ai) <a href="https://pypi.org/project/inductiva/"><img src="https://img.shields.io/pypi/v/inductiva" alt="pypi version"></a> ![coverage badge](https://raw.githubusercontent.com/inductiva/inductiva/Inductiva-badges/badges/cov.svg)

![inductiva_banner](https://github.com/inductiva/inductiva/assets/7538022/85569dd8-3beb-4ac3-8cbe-78e300a28fde)

# Inductiva: a Python package for scaling simulations on the Cloud


Welcome to the official Python library for the Inductiva API.

The Inductiva API allows running a set of open-source physical
simulators on the cloud, easily parallelizing simulations, each running
on hundreds of CPU cores.

Inductiva simplifies the complexities of cloud resource management, and software
configuration, offering a straightforward Python interface for running simulations
on state-of-the-art hardware. This allows scientists and engineers to focus their
time and energy on what matters: running simulations that solve real problems.


# System Requirements

- Python >= 3.9 
- pip (or pip3) to install the inductiva package
  
# Getting started

1. Register at:

[https://console.inductiva.ai](https://console.inductiva.ai)

2. Install the Inductiva Python Package
```
pip install inductiva
```
3. Authenticate with your API Key
```
inductiva auth login
```


To find out more, check our [documentation](https://docs.inductiva.ai/).


If you have any questions or suggestions about the API please
[open an issue on the inductiva’s API Client GitHub repo](https://github.com/inductiva/inductiva/issues),
or contact us via [support@inductiva.ai](mailto:support@inductiva.ai).
mimiqcircuits 0.18.2
User API for MIMIQ by QPerfect. Build quantum circuits, schedule remote simulations, and explore simulation results.
Requires Python: <4,>=3.9

# Python interface to MIMIQ Circuits (`mimiqcircuits`)

*A library to handle quantum circuits for **QPerfect**'s MIMIQ Emulator.*

| **Documentation** | **Build Status** |
|:----:|:----:|
| [![][docs-stable-img]][docs-stable-url] | [![][GHA-img]][GHA-url] |

## Installation

```sh
pip install mimiqcircuits

# or

pip install mimiqcircuits[visualization]
```

## Documentation

- [**STABLE**][docs-stable-url] &mdash; **documentation of the most recently version.**

## Project Status

The package is tested against, and being developed for, Python 3.8 and above
on Linux, MacOS, and Windows.

*MIMIQ Circuits* is currently in the alpha stage and open to test for early
adopters. We are continuously refining and enhance our tools so we value any
feedback you may have to ensure that MIMIQ Circuits meets and exceeds your
expectations. If you want access to MIMIQ Circuits, we encourage you register
on [our website](https://qperfect.io/index.php/mimiq-circuits).

## Related projects

- [MimiqCircuits.jl](https://github.com/qperfect-io/MimiqCircuits.jl) the Julia version of this library.

## COPYRIGHT

> Copyright © 2022-2023 University of Strasbourg. All Rights Reserved.
>
> Licensed under the Apache License, Version 2.0 (the "License");
> you may not use this file except in compliance with the License.
> You may obtain a copy of the License at
>
>     <http://www.apache.org/licenses/LICENSE-2.0>
>
> Unless required by applicable law or agreed to in writing, software
> distributed under the License is distributed on an "AS IS" BASIS,
> WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
> See the License for the specific language governing permissions and
> limitations under the License.

[docs-stable-img]: https://img.shields.io/badge/docs-stable-blue.svg
[docs-stable-url]: https://qperfect-io.github.io/mimiqcircuits-python/

[GHA-img]: https://github.com/qperfect-io/mimiqcircuits-python/workflows/Test/badge.svg
[GHA-url]: https://github.com/qperfect-io/mimiqcircuits-python/actions/workflows/test.yml