semvec/pypi-packages huggingface dataset:
histomics 0.0.1
Histomics slide(WSI) analysis toolkit
Requires Python: <4.0,>=3.10

# Histomics slide(WSI) analysis toolkit
get_histomics_features.py was adapted from *https://github.com/arunima2/histomicstk_feature_extraction*

## Usage
```
python get_histomics_features.py <path of image to analyze> <path of image to normalize the input image to> <minimum nuclei area in px for an object to be labeled nuclie> <foreground threshold to identify nuclei in grayscale> <path of folder where output files should be stored>
```
  
### Example Usage
```
python get_histomics_features.py ../prostate_images/T4--33-B6-L_L3R5_T4_22.png ../prostate_images/T4--33_B6_R_L1R6_T4_3.png 15 100 histomics_output/
```
secateur 0.2.3
A framework for migrating relational databases to document-oriented databases
Requires Python: >=3.6

# Secateur
A framework for migrating relational databases to document-oriented databases

## Introduction
Secateur is a tool for migrating data from relational databases to document-oriented ones.
It provides the ability to transfer data to the target storage, preserving the hierarchical structure of the data and modifying it to a new model representation.
The key functions are:
1. Automatic configuration of migration scenarios with flexible configuration;
2. Support for manual configuration, configuring transformations using SQL queries, JinjaSQL (DBT-like) support;
3. Data migration by nesting levels of the data schema (parallel processing, patch migration, mass dump to JSON files);
4. Pruning of target storage documents, combining them depending on the types of relationships (one-to-one, one-to-many, many-to-one, many-to-many).

### Supported DBMS
#### Relational
`PostgreSQL` `...`

#### Documentary
`MongoDB`

### Toolkits used:
`sqlalchemy` `pymongo` `jinja2`

## Documentation
Latest documentation is at:

## Installation / Requirements
`pip install secateur`

## Usage example
```
from secateur import Secateur

secateur = Secateur(
    "postgresql://user:password@localhost:5495/database",
    "mongodb://localhost:27017", mongo_database="db_name"
)

secateur.auto_config(schemes=['public'])
secateur["collection_1"] = secateur.from_query("SELECT date, SUM(price) FROM mart.orders GROUP BY date")
secateur.save_config()

secateur.backup(auto=True)
secateur.migrate(batch_size=500)

secateur.prune()

secateur.close()
```

## License
Secateur is distributed under the [MIT license](https://www.opensource.org/licenses/mit-license.php)
gshapa 0.1.10
Gene Set Analysis for Single-Cell RNA-seq Using Random Forest and SHAP Valuese
Requires Python: >=3.8
Keywords: Gene Set Analysis, Random Forest, SHAP values

# GSHAPA: Gene Set Analysis for Single-Cell RNA-seq Using Random Forest and SHAP Values

<div style="display: flex; justify-content: center;">
 <img src="logo.webp" height="250">
</div>

This Python package Gene Set Analysis for Single-Cell RNA-seq Using Random Forest and SHAP Values

### Installation:

`pip install gshapa`

### Documentation:

[gshapa.readthedocs.io](gshapa.readthedocs.io)

### GitLab repository
[gitlab repository and tutorials](https://gitlab.com/bf2i/gshapa)

### Tutorials:

Check the jupyter notebook tutorials located in the __tutorial__ folder
+ `simulator.ipynb` for the assessment of the method on synthetic data
+ `MS-dataset-RF-1percent.ipynb`, `DN-RF-1percent.ipynb`, `DII-RF-1percent.ipynb`, `COVID-RF-1percent.ipynb` and `AD-RF-1percent.ipynb` for different bio-medical applications

### Authors:

For bug reports and feedback do not hesitate to contact the authors

+ Sergio Peignier: sergio.peignier AT insa-lyon.fr
+ Sara Khademioureh: khademio AT ualberta.ca

### Maintainer:

+ Sergio Peignier: sergio.peignier AT insa-lyon.fr
azure-transcribe 0.5
A Python library for interacting with Azure Transcription API
Requires Python: >=3.6

## Project Description

Azure-Transcribe is a service that transcribes speech from audio files to text using Microsoft Azure Cognitive Services.

## Requirements

* Python (3.6 or higher)
* azure-storage-blob

## Installation
Install using pip

`pip install azure-transcribe`

## P.S.

If you find bugs, need help, or want to talk to the developers, please write an issue on [GitHub](https://github.com/DCDie/azure-transcribe/issues).
kagi-bridge-mcp 0.1.6
Kagi Bridge MCP Server
Requires Python: >=3.12

# Kagi Bridge MCP

This is a MCP (Multichannel Protocol) server that provides search capability using a headless browser. It simulates browser-based searches without requiring a Kagi API key.

## Installation

This project uses `uv` for dependency management:

```bash
# Install the package with dependencies
uv pip install .

# Install the browser binary
python -m playwright install chromium
```

## Usage

The MCP server requires a URL that contains an authentication token for the search service. This URL is used to initialize cookies that will be used for subsequent searches.

You can provide the URL in two ways:

1. Environment variable:
```bash
export SEARCH_URL="https://kagi.com/your_token_url"
kagi-bridge-mcp
```

2. Command-line argument:
```bash
kagi-bridge-mcp --url "https://kagi.com/your_token_url"
```

## Features

- Browser-based search that simulates real user interaction
- Handles 302 redirects and cookie-based authentication
- Compatible with existing MCP format for seamless integration

## Development

```bash
# Set up a development environment
uv venv
source .venv/bin/activate
uv sync
```