semvec/pypi-packages huggingface dataset:
mechanism-learn 2.3.2
Mechanism-learn is a simple method to deconfound observational data such that any appropriate machine learning model is forced to learn predictive relationships between effects and their causes, despite the potential presence of multiple unknown and unmeasured confounding. The library is compatible with most existing ML deployments. The library is compatible with most existing ML deployments such as models built with Scikit-learn and Keras.
Requires Python: >=3.9

To run the experiment code, please install the mechanism-learn package using the distribution file 'mechanism_learn-2.2.1-py3-none-any.whl' in the 'dist' folder.

In Python, use: import mechanism_learn.pipeline to import the mechanism learning algorithms.

Please note that due to the limit of file size, we cannot attach all experiment data, such as ICH CT scans and the Background-MNIST data. The original ICH data is available at https://physionet.org/content/ct-ich/1.3.1/. For the Background-MNIST data, it is modified from the original MNIST dataset. This semi-synthetic Background MNIST dataset can be generated by using the Python code file: .\test_data\semi_synthetic_data\semi-synthetic_data_gen.py.
jiri-destkird 0.0.8
Jiri Destkird: The ultimate Python toolkit for streamlining Deep Learning & Computer Vision workflows.
Requires Python: >=3.8

# Jiri Destkird (ژیرى ده‌ستكرد) 🧠✨

A Python library for Deep Learning (DL) and Computer Vision (CV) utilities.

The name "Jiri Destkird" comes from the Kurdish words **"Jirî"** (meaning smart, clever, intelligent) and **"Destkird"** (meaning man-made, artificial), translating to **Artificial Intelligence** in the Kurdish language used in Kurdistan.

## Purpose

The goal of this project is to simplify and streamline common, everyday tasks in Deep Learning and Computer Vision, aiming for cleaner and more efficient code.

## Installation

```bash
pip install jiri_destkird
hakiix 0.1.1
Hakiix console logger module
Requires Python: >=3.10

# Hakiix

Hakiix เป็น **Python console logger module** สำหรับพิมพ์ log สีสันสดใสใน console  
เหมาะสำหรับงานสคริปต์, automation, หรือโปรเจกต์ Python ที่ต้องการ log อ่านง่ายและสวยงาม

สวัสดีครับ โมดูลนี้เป็น **โมดูลส่วนตัวที่เปิดให้ทุกคนใช้งานได้**  

---

## Installation

โมดูลสามารถติดตั้งได้ 2 วิธี

### 1️⃣ ติดตั้งผ่าน Git
ก่อนอื่นต้องติดตั้ง Git ก่อน  

**Windows:**  
[ดาวน์โหลด Git](https://git-scm.com/downloads)

**Linux:**  
```bash
sudo apt install git -y
```

**MacOS:**  
```bash
brew install git
```

ติดตั้งโมดูลผ่าน Git:
```bash
pip install git+https://github.com/daimondz2/hakiix.git
```

### 2️⃣ ติดตั้ง
```bash
pip install hakiix
```

---

## Usage Example

### ใช้ console instance

```python
from hakiix import console

console.info("เริ่มทำงาน script")
console.suc("ทำงานสำเร็จ")
console.err("เกิดข้อผิดพลาด")
console.warn("เตือนความผิดพลาด")
console.dbg("Debug ข้อมูลตัวแปร x=123")
console.token("1234567890abcdefghijklmnopqrstuvwxyz")
```

### ใช้ shortcut แบบสั้น

```python
from hakiix.console import log_info, log_success, log_error

log_info("Info ผ่าน shortcut")
log_success("Success ผ่าน shortcut")
log_error("Error ผ่าน shortcut")
```

---

## Output ตัวอย่าง

```
[ INFO ] 12:34:56 | เริ่มทำงาน script
[ SUCC ] 12:34:56 | ทำงานสำเร็จ
[ ERRO ] 12:34:56 | เกิดข้อผิดพลาด
[ WARN ] 12:34:56 | เตือนความผิดพลาด
[ DEBG ] 12:34:56 | Debug ข้อมูลตัวแปร x=123
[ TOKN ] 12:34:56 | 1234567890abcdefghijklmn...
```

---

## ฟังก์ชัน clear

ล้างหน้าจอ console:
```python
console.clear()
```

---

## หมายเหตุ

- รองรับ Python 3.x  
- สี Neon ใช้งานได้บน terminal ที่รองรับ ANSI codes  
- สามารถใช้งานบน Windows, Linux, MacOS  
- Shortcut log ทำให้เรียกใช้ง่ายขึ้น  

---

## License

MIT License
markus-autotesting-core 0.1.0
Core functionality for Markus Autotesting
Requires Python: >=3.9
Keywords: autotesting, markus

# markus-autotesting-core

`markus-autotesting-core` is a Python package containing core utilities for the MarkUs autotester. In particular, it defines common data types used by different components of the autotester.

## Installation
To install the package, you can use pip:

```
pip install markus-autotesting-core
```

## Usage
After installation, you can use the package in your Python scripts. Here is a simple example:

```python
from markus_autotesting_core import core

# Example usage of core functionality
result = core.some_function()
print(result)
```

## Running the Package
You can run the package as a script using the following command:

```
python -m markus_autotesting_core
```

## Testing
To run the tests, navigate to the project directory and use:

```
pytest tests/test_core.py
```

## Contributing
Contributions are welcome! Please feel free to submit a pull request or open an issue for any enhancements or bug fixes.

## License
This project is licensed under the MIT License. See the LICENSE file for more details.
flowdb 0.1.3
The AI-Native Database: JSON + Vectors + MCP
Requires Python: >=3.10
Keywords: agent, ai, database, mcp, rag, vector

# FlowDB

FlowDB is a JSON-based database stack that is AI-Native.

It's core objective is to be fast, simple, and n8n + AI optimized.

-----

## Core Features

- JSON (No-SQL)
- API controlled for n8n-usage and database safety
- API Key protected
- Dockerized for ease of use
- MCP server for Agentic use
- Vectorization built in (BYOK)
- Client CLI and PyPi package
- Speed: Built on LMDB (Lightning Memory-Mapped Database) and USearch (HNSW Vector Search).

-----

## Guidelines

FlowDB is intentionally built to be fast, simple, and AI-Native. It is **NOT** ideal for compliance applications such as
FinTech because it lacks SQLs ACID safety measures.

FlowDB is ideal for AI applications, automation, speed, and n8n workflows.

-----

## Getting Started

### GitHub: https://github.com/RecursionAI/FlowDB

### [Install and Setup](./docs/install.md)

Full guide to install both the Client SDK and configure the dockerized server.

### [Client SDK](./docs/sdk.md)

How to use the client SDK to interact with the database in your code and utilyze Pydantic models

### [Full n8n Guide](./docs/n8n.md)

Comprehensive guide for implementing FlowDB in your n8n workflows for and open-source persistent memory adn VectorDB
solution.

### [URL Schemas](./docs/schemas.md)

URL schemas and API guidelines.


### [MCP Server](./docs/mcp.md)
How the MCP server works and how to set it up so Claude Desktop can use it and interact with your db.

-----
cuws 0.0.3
CuWS implements watershed segmentation from minima on GPUs.
Requires Python: >=3.11

# CuWS

GPU-accelerated watershed segmentation from minima using CuPy.

This implements only the watershed from minima algorithm, not the seeded watershed.

## Installation

```bash
pip install cuws
```

## Usage

```python
from cuws import watershed_from_minima

...

labels = watershed_from_minima(image, mask)
```

## Requirements

- Python >= 3.11
- CUDA-compatible GPU
- CuPy