semvec/pypi-packages huggingface dataset:
mlinsightlab 0.0.36
Your Open Source Data Science and MLOps Platform
Requires Python: >=3.6

# MLIL Python Client

![License](https://img.shields.io/github/license/mlinsightlab/mlinsightlab-python-sdk)
![PyPI](https://img.shields.io/pypi/v/mlinsightlab)
![GitHub stars](https://img.shields.io/github/stars/mlinsightlab/mlinsightlab-python-sdk?style=social)

This package allows you to more easily interact with the MLIL platform from a Python interface.

Please note that this is a supplement to the JupyterLab instance in the platform and, as such, 
is not intended to replicate the end-to-end data science workflow that MLIL enables. Rather, this 
client is designed to make it easier to make requests to MLIL's model invocation, model management, 
and administrative API endpoints.

## Basic usage

### Installation
```bash
pip install mlinsightlab
```
### Getting started
When first creating a MLILClient object, you'll be prompted to input your MLIL platform credentials, 
including the API key that you have been issued to interact with the platform. This information will 
be stored in a configuration file located at ```HOME/.mlil/config.json```.

```python
    >>> from mlinsightlab import MLILClient
    # creates a new client object
    >>> client = mlil.MLILClient() 
```
Now that you've logged in once, you'll be able to use these saved credentials to create MLILClient 
objects more easily in the future.

### Basic usage
Now that you're authenticated, you can more easily interact with your deployment of MLIL!
```python
# list all users on the platform
client.list_users()

# create a new user in the platform
client.create_user(role = 'user', api_key='mmm',username='Homer.Simpson', password='Doughnuts!')

# double-check a user's role
client.get_user_role(username='Homer.Simpson')

# verify a user's password

# issue a user a new password
client.issue_new_password(new_password='new_password') # by default updates the config.json file

# issue a user a new API key
client.issue_api_key(username='Homer.Simpson',password='new_password') # by default updates the config.json file

# delete a user
client.delete_user(username='Homer.Simpson', verbose=True
```

Now that your platform users are all set, it's time to manage and use your models.

```python
# list models
client.list_models()

# predict - in this case model flavor is transformers, but you can use e.g. pyfunc, etc.
client.predict(model_name='GPT-AGI', model_flavor='transformers', model_version_or_alias='1',data='Hello AI overlord!'
```
If you've been working on the public library's computers, or just want to erase the ```config.json``` 
file containing your credentials, you can also do so via the Python client.

```python
client.purge_credentials()
```
gemini-application 0.3.2
Core application library of the GEMINI Digital Twin
Requires Python: <4.0,>=3.11

[![Test workflow status](https://github.com/GEMINI-Digital-Twin/gemini-application/actions/workflows/python_test.yaml/badge.svg)](https://github.com/GEMINI-Digital-Twin/gemini-application/actions/workflows/python_test.yaml)
[![Lint workflow status](https://github.com/GEMINI-Digital-Twin/gemini-application/actions/workflows/python_lint.yaml/badge.svg)](https://github.com/GEMINI-Digital-Twin/gemini-application/actions/workflows/python_lint.yaml)


# gemini-application

This repository is part of the 'Nieuwe Warmte Nu GEMINI Digital Twin' project. 

Application repository of the GEMINI Digital Twin


## Bug reports and discussions

If you think you found a bug, feel free to open an [issue](https://github.com/GEMINI-Digital-Twin/gemini-application/issues).
Focused suggestions and requests can also be opened as issues. Before opening a pull request, please start an issue or a discussion on the topic.

## Contribution

If you want to contribute to the package, please read our [CONTRIBUTING.md](https://github.com/GEMINI-Digital-Twin/gemini-application/blob/main/CONTRIBUTING.md) file.

## License

This content is released under the [GNU General Public License v3.0](https://www.gnu.org/licenses/gpl-3.0.html) License.


## Acknowledgments
This project is sponsored by NWM innovatie: Datagedreven optimalisatie aardwarmtesystemen
(https://nwn.nu/projecten/innovaties/datagedreven-optimalisatie-aardwarmte-systemen)

Organization: TNO, Aardyn, HVC, Well Engineering Partners (WEP), Helin Data
pr-install-pack 0.1.0
Prueba de instalacion de paquete
Requires Python: >=3.11

https://chatgpt.com/c/68e55ff6-72c4-8332-b31a-16557791346a

uv build

uv pip install .

Si quieres editable (desarrollo):
uv pip install -e .


uv pip install ..\pr_install_pack\dist\pr_install_pack-0.1.0-py3-none-any.whl
uv pip install ..\pr_install_pack\dist\*.whl --force-reinstall


# con uv pip
Si lo instalaste desde wheel (.whl)
uv build
uv pip install -e ..\pr_install_pack
uv pip install ..\pr_install_pack\dist\pr_install_pack-0.1.0-py3-none-any.whl
uv pip uninstall ..\pr_install_pack\dist\pr_install_pack-0.1.0-py3-none-any.whl


# solo con uv
uv add ..\pr_install_pack\dist\pr_install_pack-0.1.0-py3-none-any.whl
uv add --editable ..\pr_install_pack
uv remove pr_install_pack
twosamplemr 0.1.3
Two-sample Mendelian randomization implemented in Python
Requires Python: >=3.8

# TwoSampleMR-Python
A package for performing Mendelian randomization using GWAS summary data. It allows causal inference using genetic variants as instrumental variables, fully integrated with the [IEU OpenGWAS](https://opengwas.io/) database.


This repository contains a reimplementation of the [TwoSampleMR](https://github.com/MRCIEU/TwoSampleMR) package.  
It was developed as part of a master's thesis at UniZg-FER for academic purposes and learning.  


[![PyPI version](https://badge.fury.io/py/twosamplemr.svg)](https://pypi.org/project/twosamplemr/)


| Module | Description |
|--------|--------------|
| `instruments.py` | Extracts instruments from OpenGWAS. |
| `query.py` | Queries outcome data from OpenGWAS. |
| `harmonise.py` | Harmonises exposure and outcome datasets. |
| `mr.py` | Core MR methods (IVW, Egger, Median, Mode, GRIP). |
| `heterogeneity.py` | Heterogeneity and pleiotropy tests. |
| `multivariable_mr.py` | Multivariable MR framework. |




## Installation
```python
pip install twosamplemr
```

## Example Usage:

```python
from twosamplemr.instruments import extract_instruments
from twosamplemr.query import extract_outcome_data
from twosamplemr.harmonise import harmonise_data
from twosamplemr.mr import mr

exposure = extract_instruments(["ieu-a-2"])
outcome = extract_outcome_data(exposure["SNP"], ["ieu-a-7"])
dat = harmonise_data(exposure, outcome)
res = mr(dat)
print(res)
```

*This project is based on the original R implementation by Gibran Hemani, Philip Haycock, Jie Zheng, Tom Gaunt, Ben Elsworth, Tom Palmer, available at [MRC IEU TwoSampleMR](https://github.com/MRCIEU/TwoSampleMR), licensed under the MIT License.*

MIT License © 2025 Jurica Matosic
AstroForge 0.3.1
A Python library of astrodynamics tools
Requires Python: >=3.12
Keywords: astrodynamics, space

<!-- These are examples of badges you might want to add to your README:
     please update the URLs accordingly

[![Built Status](https://api.cirrus-ci.com/github/<USER>/AstroForge.svg?branch=main)](https://cirrus-ci.com/github/<USER>/AstroForge)
[![ReadTheDocs](https://readthedocs.org/projects/AstroForge/badge/?version=latest)](https://AstroForge.readthedocs.io/en/stable/)
[![Coveralls](https://img.shields.io/coveralls/github/<USER>/AstroForge/main.svg)](https://coveralls.io/r/<USER>/AstroForge)
[![PyPI-Server](https://img.shields.io/pypi/v/AstroForge.svg)](https://pypi.org/project/AstroForge/)
[![Conda-Forge](https://img.shields.io/conda/vn/conda-forge/AstroForge.svg)](https://anaconda.org/conda-forge/AstroForge)
[![Monthly Downloads](https://pepy.tech/badge/AstroForge/month)](https://pepy.tech/project/AstroForge)
[![Twitter](https://img.shields.io/twitter/url/http/shields.io.svg?style=social&label=Twitter)](https://twitter.com/AstroForge)
-->

# AstroForge

<p align="center">A Python library of astrodynamics tools.</p>

AstroForge is meant to be used for satellite state propagation and the computation of observations from ground-based or space-based sensors. Precise orbit fitting requires pristine knowledge of the observer’s site location in inertial coordinates, which in turn requires methodical handling of coordinate systems and the conversions between them. The goal of AstroForge is to make all of this **easy**.

## Installation

From this repository's top-level directory, run the command

```console
pip install .
```

## Usage

For details on how to use the various features of `AstroForge`, we recommend following the Jupyter notebooks in the `examples/` directory.

You can also read the project's documentation [here](https://astroforge.readthedocs.io/en/latest/).

## Citation

Please use this DOI number reference, published on [Zenodo](https://zenodo.org), when citing the software:

[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.15066088.svg)](https://doi.org/10.5281/zenodo.15066088)

## Disclaimer

DISTRIBUTION STATEMENT A. Approved for public release. Distribution is unlimited.

Research was sponsored by the United States Air Force Research Laboratory and the United
States Air Force Artificial Intelligence Accelerator and was accomplished under Cooperative
Agreement Number FA8750-19-2-1000. The views and conclusions contained in this document
are those of the authors and should not be interpreted as representing the official
policies, eitherexpressed or implied, of the United States Air Force or the U.S.
Government. The U.S.Government is authorized to reproduce and distribute reprints
for Government purposes notwithstanding any copyright notation herein.

© 2024 Massachusetts Institute of Technology.
simpleconfigfinder 0.5.1
Minimalistic configuration finder with standard Python libraries.
Requires Python: >=3.11

# Config Finder
Finds a configuration file (e.g. pyproject.toml) and returns some sub-configuration by only using python standard libraries.


Supported formats:

* [TOML](https://en.wikipedia.org/wiki/TOML)
* [JSON](https://en.wikipedia.org/wiki/JSON)
* [INI](https://en.wikipedia.org/wiki/INI_file)
* [YAML](https://en.wikipedia.org/wiki/YAML) (see [Documentation](https://fabfabi.github.io/simpleconfigfinder/) how to enable)


## Use Case
When defining machine learning projects and handling the project configuration by e.g. a [pyproject.toml](https://packaging.python.org/en/latest/guides/writing-pyproject-toml/) (e.g. with [Astral UV](https://docs.astral.sh/uv/) or  [Poetry](https://python-poetry.org/)) you can utilize the configuration files to define and store important variables.

Instead of defining global variables in your code or using [dotenv](https://pypi.org/project/python-dotenv/), a configuration file such as the pyproject.toml can be used for those values.

## Example
Instead of defining global variables, just define these parameters in the pyproject.toml

    [tool.some_tool]
    key1 = "some_value_1"
    key2 = "some_value_2"

    [tool.some_tool.default_config]
    important_key = "some_value"

    [tool.some_tool.special_config]
    important_key = "another_value"



To get the key for your `default_config` just call

```python
find_configuration("pyproject.toml", ["tool", "some_tool", "default_config", "important_key"])
```

    "some_value"

and respectively for your `special_config` use

```python
find_configuration("pyproject.toml", ["tool", "some_tool", "special_config", "important_key"])
```

    "another_value"

Or you could get one full set of the configuration as dictionary via

```python
find_configuration("pyproject.toml", ["tool", "some_tool", "special_config"])
```

    { "important_key" : "another_value" }


[Link to full documentation](https://fabfabi.github.io/simpleconfigfinder/)
snitch 0.1.0
Keeping tabs on your systems..
Requires Python: >=3.12

# Snitch
[![CodeQL](https://github.com/jake-ps/snitch/actions/workflows/code-ql-analysys.yml/badge.svg)](https://github.com/jake-ps/snitch/actions/workflows/code-ql-analysys.yml)

Keeping tabs on your systems..

## Description

Snitch is a tool designed to provide a common interface for monitoring system activity. It uses a plugin-based system to check for signs of life, such as file modifications or the output of custom functions. This allows you to get a quick and simple "active" or "inactive" status for a machine.

## CLI Usage

Snitch offers a few simple commands to manage activity checks and view status.

### Checking for Activity

To run a new activity check, use the `check` command. This will execute all configured plugins, update the local state file with the results, and print the current status.
**Note:** You'll likely want to run this with a user that has root permissions to make sure it has access to each file.

```bash
snitch check
```

### Displaying the Current Status

To view the most recent activity status without running a new check, use the `status` command.

```bash
snitch status
```

Example output:
```
--- System Activity Status ---
Status: ACTIVE (within last 5 days)
Confidence: 100.0%
Last File Activity: 2025-09-19 10:30:00
Last Checked: 2025-09-19 10:30:00
```

### Serving the Status via API

Snitch can run as a simple HTTP server to expose the latest activity status via a JSON API. Use the `serve` command for this.

```bash
snitch serve --port 8000
```

This will start a server on port 8000. You can then query the API to get the status:

```bash
curl http://localhost:8000/
```
nonebot-plugin-auto-enter-group 1.0.1
NoneBot2 加群自动审批
Requires Python: >=3.9
Keywords: nonebot2, nonebot-plugin-auto-enter-group

<div align="center">
  <a href="https://v2.nonebot.dev/store"><img src="https://github.com/A-kirami/nonebot-plugin-template/raw/resources/nbp_logo.png" width="180" height="180" alt="NoneBotPluginLogo"></a>
  <br>
  <p><img src="https://github.com/A-kirami/nonebot-plugin-template/raw/resources/NoneBotPlugin.svg" width="240" alt="NoneBotPluginText"></p>
</div>

<div align="center">

# nonebot-plugin-auto-enter-group

_✨ NoneBot2 加群自动审批 ✨_


<a href="./LICENSE">
    <img src="https://img.shields.io/github/license/padoru233/nonebot-plugin-auto-enter-group.svg" alt="license">
</a>
<a href="https://pypi.python.org/pypi/nonebot-plugin-auto-enter-group">
    <img src="https://img.shields.io/pypi/v/nonebot-plugin-auto-enter-group.svg" alt="pypi">
</a>
<img src="https://img.shields.io/badge/python-3.9+-blue.svg" alt="python">

</div>

帮助管理员审核入群请求，退群自动记录拒绝入群

## 📖 介绍

在群内播报申请和审核状态，添加关键词后自动进行模糊匹配。  
可开启退群黑名单，自动拒绝退群的人再次申请。

## 💿 安装

<details open>
<summary>使用 nb-cli 安装</summary>
在 nonebot2 项目的根目录下打开命令行, 输入以下指令即可安装

    nb plugin install nonebot-plugin-auto-enter-group

</details>

<details>
<summary>使用包管理器安装</summary>
在 nonebot2 项目的插件目录下, 打开命令行, 根据你使用的包管理器, 输入相应的安装命令

<details>
<summary>pip</summary>

    pip install nonebot-plugin-auto-enter-group
</details>
<details>
<summary>pdm</summary>

    pdm add nonebot-plugin-auto-enter-group
</details>
<details>
<summary>poetry</summary>

    poetry add nonebot-plugin-auto-enter-group
</details>
<details>
<summary>conda</summary>

    conda install nonebot-plugin-auto-enter-group
</details>

打开 nonebot2 项目根目录下的 `pyproject.toml` 文件, 在 `[tool.nonebot]` 部分追加写入

    plugins = ["nonebot_plugin_auto_enter_group"]

</details>

## ⚙️ 配置

无配置项，涉及到群管需用命令进行配置
插件数据存储在目录 ``~/.local/share/nonebot2/nonebot_plugin_auto_enter_group``

## 🎉 使用
### 指令表
| 指令 | 权限 | 需要@ | 范围 | 说明 |
|:-----:|:----:|:----:|:----:|:----:|
| 查看关键词 | 群主/管理员/超级用户 | 否 | 群聊 | 群主/管理员可查看入群关键词 |
| 添加/删除允许关键词 | 群主/管理员/超级用户 | 否 | 群聊 | <关键词> |
| 添加/删除拒绝关键词 | 群主/管理员/超级用户 | 否 | 群聊 | <关键词> |
| 启用/禁用退群黑名单 | 群主/管理员/超级用户 | 否 | 群聊 | 启用后退群用户将无法再次加入 |
| 查看黑名单 | 群主/管理员/超级用户 | 否 | 群聊 | 查看当前群组黑名单 |
| 添加/移除黑名单 | 群主/管理员/超级用户 | 否 | 群聊 | 将批量用户ID添加/移出黑名单 |

## 🌹 鸣谢

感谢 [大橘](https://github.com/zhiyu1998) 提供的代码，多写点让我抄抄！  
由于我不是代码相关专业，使用AI协助完成的代码
hdf5plugin 6.0.0
HDF5 Plugins for Windows, MacOS, and Linux
Requires Python: >=3.9

hdf5plugin
==========

.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.7257761.svg
   :target: https://doi.org/10.5281/zenodo.7257761

*hdf5plugin* provides `HDF5 compression filters <https://github.com/HDFGroup/hdf5_plugins/blob/master/docs/RegisteredFilterPlugins.md#list-of-filters-registered-with-the-hdf-group>`_ (namely: Blosc, Blosc2, BitShuffle, BZip2, FciDecomp, LZ4, Sperr, SZ, SZ3, Zfp, ZStd) and makes them usable from `h5py <https://www.h5py.org>`_.

See `documentation <http://www.silx.org/doc/hdf5plugin/latest/>`_.

Installation
------------

To install, run::

     pip install hdf5plugin [--user]
     
or, with conda (https://anaconda.org/conda-forge/hdf5plugin)::

    conda install -c conda-forge hdf5plugin

To install from source and recompile the HDF5 plugins, run::

     pip install hdf5plugin --no-binary hdf5plugin [--user]

Installing from source can achieve better performances by enabling AVX2 and OpenMP if available.

For more details, see the `installation documentation <http://www.silx.org/doc/hdf5plugin/latest/install.html>`_.

How-to use
----------

To use it, just use ``import hdf5plugin`` and supported compression filters are available from `h5py <https://www.h5py.org>`_.

For details, see `Usage documentation <http://www.silx.org/doc/hdf5plugin/latest/usage.html>`_.

License
-------

The source code of *hdf5plugin* itself is licensed under the `MIT license <LICENSE>`_.

Embedded HDF5 compression filters are licensed under different open-source licenses:
see the `license documentation <http://www.silx.org/doc/hdf5plugin/latest/information.html#license>`_.