semvec/pypi-packages huggingface dataset:
normal-modes-tools 1.0.6
Tools for working with normal modes
Requires Python: >=3.13
Keywords: chemistry

# `normal_modes_tool`
Python tools helpful in working with normal modes.

## Capabilities
- List normal modes and their frequencies
```bash
# Works only if the comment line in the xyz files has the following format
# 0. Vibration mode, 64.77 cm-1, B1
# i.e., frequency is the fourth whitespace-separated item of the comment line
python -m normal_modes_tool --list nmodes.xyz
```
- Displace the molecular geometry along a selected normal mode, see
  `test/displace/displace.py`.
- Compare two sets of normal modes and their harmonic frequencies, including
  building of the matrix of normal mode's overlaps, i.e., the Duszyński matrix
  `test/sroph/build_duszynski.py`.
```bash
python -m normal_modes_tool --compare new_nmodes.xyz old_nmodes.xyz
```
- Express the difference between two molecular geometries as displacement along
  normal modes, see `test/pyrazine/find_dQ.py`.
- Calculate the Huang-Rhys factors, see `test/pyrazine/print_HRf.py` or
  `test/sroph/hrfs/huang_rhys_factors.py`
- Find normal modes for molecule with isotope substitutions, see
  `test/xsim/deuterate_nmodes.py`.
- Transform gradients expressed in one set of normal modes to another, see
  `test/xsim/deuterate_xsim_gradient.py`.
wagtail-oidc-provider 0.1.0
Bridging django-oidc-provider to wagtail admin CMS
Requires Python: >=3.6
Keywords: django, wagtail, oidc, sso

# wagtail-oidc-provider

Pairs with [django-oidc-provider](https://github.com/juanifioren/django-oidc-provider/), with wagtail support.

## Install

```
pip install wagtail-oidc-provider
```

Add `wagtail_oidc_provider` to your `INSTALLED_APP`:

```py
INSTALLED_APP = [
    ...
    "oidc_provider",
    "wagtail_oidc_provider",
    ...
]
```

and configuration snippet will popup in the admin navbar of wagtail!
webiks-hebrew-ragbot 1.4.0
A search engine using machine learning models and Elasticsearch for advanced document retrieval.
Requires Python: >=3.10

# **Webiks-Hebrew-RAGbot**

## **Overview**

This project is a search engine that uses machine learning models and Elasticsearch to provide advanced document retrieval.
You can use [Webiks-Hebrew-RAGbot-Demo](https://github.com/NNLP-IL/Webiks-Hebrew-RAGbot-Demo) to demonstrate the engine's document retrieval abilities

## **Features**

Document representation and validation
Document embedding and indexing in Elasticsearch
Advanced search using machine learning model
Integration with LLM (Large Language Model) client for query answering

## **Installation**

1.  Clone the repository:
   
`git clone https://github.com/NNLP-IL/Webiks-Hebrew-RAGbot.git`

`cd Webiks-Hebrew-RAGbot`

2.  Create a virtual environment and activate it:Â Â 

`python -m venv venv`

`source venv/bin/activate`

On Windows use `\venv\\Scripts\\activate\`

3.  Install the required dependencies:Â Â 

`pip install -r requirements.txt`

## **Configuration**

Set the following environment variables:Â Â 

MODEL_LOCATION: Path to the model directory 
ES_EMBEDDING_INDEX_LENGTH: Size of any index in elasticsearch
EMBEDDING_INDEX: The name of the index we will embed docs into
hunyuan-image-mcp-test 0.1.6
Hunyuan Image MCP
Requires Python: >=3.10
Keywords: hunyuan, image, mcp

这是一个简单的 MCP Server 示例，通过标准输入输出与 MCP 客户端进行通信。

## 安装

```bash
pip install hunyuan-image-mcp
```

## 使用

通过 uvx 运行：

```
uvx hunyuan-image-mcp
```
tablesage 0.0.8
A profiling tool for tabular data leveraging the powerful capabilities of LLMs, appropriate for data scientists.
Requires Python: >=3.6

# TableSage
A profiling tool for tabular data leveraging the powerful capabilities of LLMs, appropriate for data scientists.

# Features

<p align="center">
  <img src="img/table_features.png" width="45%" alt="Table-related features">
  <img src="img/attribute_features.png" width="45%" alt="Attribute-related features">
</p>

- **Table-related features**: Table-related features of TableSage.
- **Table-related features**: Attribute-related features of TableSage. Dashed lines indicate optional arguments, but at least one should be provided.

<table>
  <tr>
    <th>Feature</th>
    <th>Sub-Feature</th>
    <th>Input</th>
    <th>Output</th>
  </tr>
  <tr>
    <td rowspan="2">Table</td>
    <td>Table Summarization</td>
    <td>Table</td>
    <td>Property</td>
  </tr>
  <tr>
    <td>Table Type Annotation</td>
    <td>Table</td>
    <td>Property</td>
  </tr>
  <tr>
    <td rowspan="2">Column</td>
    <td>Column Summarization</td>
    <td>Table</td>
    <td>Property</td>
  </tr>
  <tr>
    <td>Column Type Annotation</td>
    <td>Table</td>
    <td>Property</td>
  </tr>
  <tr>
    <td rowspan="3">Extraction</td>
    <td>Insights Extraction</td>
    <td>List of Official Properties (Optional) <br/> List of Generated Properties (Optional) <br/> (At least one)</td>
    <td>List of Insights</td>
  </tr>
  <tr>
    <td>Spatial Information Extraction</td>
    <td>Table Description</td>
    <td>Property</td>
  </tr>
  <tr>
    <td>Temporal Information Extraction</td>
    <td>Table Description</td>
    <td>Property</td>
  </tr>
  <tr>
    <td>Comparison</td>
    <td>Properties Comparison</td>
    <td>List of Official Properties <br/> List of Generated Properties</td>
    <td>List of Comparisons</td>
  </tr>
  <tr>
    <td rowspan="2">Fusion</td>
    <td>Alternative Properties Fusion</td>
    <td>List of Official Properties (Optional) <br/> List of Generated Properties (Optional) <br/> List of Insights (Optional) <br/> (At least one)</td>
    <td>Property</td>
  </tr>
  <tr>
    <td>Complementary Properties Merging</td>
    <td>List of Official Properties (Optional) <br/> List of Generated Properties (Optional) <br/> (At least one)</td>
    <td>Property</td>
  </tr>
</table>




# Quickstart
## Installation
pip install tablesage

## Usage
You can easily use TableSage by:
```
from tablesage import TableSage

p = TableSage()
p.load_dataset(path=<path>, separator=<separator>)
profile = p.profile_dataset(<model>, endpoint=<openai-endpoint>, token=<token>)
```

You can find more information about OpenAI endpoints [here](https://github.com/openai/openai-python).

# Acknowledgements
This work was partially funded by the EU Horizon Europe projects STELAR (GA. 101070122)