semvec/pypi-packages huggingface dataset:
ini-safedb-sdk 1.0.0
Python SDK for database encryption/decryption via Java bridge server
License: MIT
Requires Python: <4.0.0,>=3.8.1
Keywords: database, encryption, security, crypto

# INISafeDB SDK (Python)

데이터베이스 암호화를 위한 Python 클라이언트 라이브러리

## 개요

Java Bridge Server를 호출하여 데이터베이스 암호화/복호화 기능을 제공하는 Python SDK입니다.

## 기술 스택

- Python 3.8+
- Poetry (패키지 관리)
- requests (HTTP 클라이언트)

## 설치

```bash
pip install ini-safedb-sdk
```

## 사용 예제

```python
from ini_safedb_sdk import INISafeDBSDK

# 클라이언트 초기화
with INISafeDBSDK(base_url="http://localhost:9081") as client:
    # 암호화
    encrypted = client.encrypt("user1", "users", "ssn", b"123-45-6789")

    # 복호화
    decrypted = client.decrypt("user1", "users", "ssn", encrypted)
    print(decrypted.decode('utf-8'))
```

## 시작하기

상세한 개발 가이드는 [PHASE2_PYTHON_SDK.md](../docs/PHASE2_PYTHON_SDK.md)를 참조하세요.

## 개발

```bash
# 의존성 설치
poetry install

# 테스트
poetry run pytest

# 빌드
poetry build
```

## PyPI 배포

```bash
poetry publish
```
InformativeFeatureSelection 3.1.3
Package which provides an feature selection algorithm which considers class separability and an implementation of Informative Normalized Difference Index (INDI)
License: MIT

# Install

`pip install InformativeFeatureSelection`

## Features

* Several implementations of feature selection algorithms based on discriminant analysis
* Binary implementation of Informative Normalized Difference Index (INDI)
* Multiclass implementation of INDI 

INDI may be extremely usefully in hyperspectral imaging analysis.

Implemented algorithms were proposed in the following papers:
1. [Paringer RA, Mukhin AV, Kupriyanov AV. Formation of an informative index for recognizing specified 
objects in hyperspectral data. Computer Optics 2021; 45(6): 873-878. DOI: 10.18287/2412-6179-CO-930.](http://www.computeroptics.ru/KO/PDF/KO45-6/450611.pdf)

2. [Mukhin, A., Paringer, R. and Ilyasova, N., 2021, September. Feature selection algorithm with feature space
separability estimation using discriminant analysis. In 2021 International Conference on Information Technology
and Nanotechnology (ITNT) (pp. 1-4). IEEE.](https://ieeexplore.ieee.org/document/9649144)

## Requirements

To simplify usage, two Docker images were created:

1. `banayaki/feature-selection:base`
This image serves as a base image for InformativeFeatureSelection.
It includes the necessary `python` and its packages.

2. `banayaki/feature-selection:notebook`
This image serves as an extension of the base image for InformativeFeatureSelection.
It includes an additional tool: Jupyter Notebook.
The Jupyter server starts automatically when the container begins.

### How to use them?

Just run the following command:

```bash
docker container run --rm -p 8888:8888 -v ./project:/home/workdir banayaki/feature-selection:notebook
```

Then just copy jupyter's token from container's log.


## Usage example

See jupyter notebook file in `examples` folder. 

## License

[MIT License](LICENSE)