semvec/pypi-packages huggingface dataset:
secugenpy 1.0.1
Secugenpy est un module Python conçu pour la capture et la comparaison d'empreintes digitales à l'aide des lecteurs biométriques SecuGen.
Requires Python: >=3.10

# 🧬 Wrapper Python du SDK SecuGen FDx Pro

Ce projet est un **wrapper Python** utilisant le SDK **SecuGen FDx Pro** sous **Windows**.  
Il permet de **capturer des empreintes digitales**, et de **comparer deux empreintes** de manière fiable et rapide.

---

## ⚙️ Prérequis

### 🖥 Système

- Windows 10 ou supérieur (64 bits)
- Pilote Windows Secugen installé
- Lecteur USB d’empreintes digitales **SecuGen**
- Fichiers `.dll` du SDK **Secugen FDx Pro** placés dans le répertoire du projet

### 🐍 Environnement Python

- Python **3.10+**

---

## 🧠 Fonctions principales

### 📌 `capturerEmpreinteDigitale()`

Initialise le lecteur **SecuGen**, capture une empreinte digitale, génère un **template encodé en base64**, et enregistre localement l’image de l’empreinte.

#### ✅ Exemple d’utilisation

```python
from secugenpy import secugen

resultat = secugen.capturerEmpreinteDigitale()

print(resultat)
```

---

### 📌 `comparaisonEmpreintesDigitales(premierEmpreinteDigitaleBase64, deuxiemeEmpreinteDigitaleBase64, niveauDeSecurite)`

Compare deux **empreintes digitales** fournies sous forme de **templates encodés en base64**.  
Utilise l’algorithme de correspondance du SDK **SecuGen FDx Pro** pour évaluer leur similarité selon le niveau de sécurité spécifié.

#### 🧾 Paramètres

| Paramètre           | Type   | Description                                                             |
|---------------------|--------|-------------------------------------------------------------------------|
| `premierEmpreinteDigitaleBase64`  | `str`  | Template de la première empreinte (encodé en base64)                    |
| `deuxiemeEmpreinteDigitaleBase64`  | `str`  | Template de la seconde empreinte (encodé en base64)                     |
| `niveauDeSecurite`  | `str`  | Niveau de sécurité                     |

#### 📄 Différents niveaux de sécurité

| Chaîne de caractère | Niveau | Description |
|---------------------|--------|-------------|
| `NONE`              | 0      | Aucun niveau de sécurité |
| `LOWEST`            | 1      | Sécurité extrêmement faible |
| `LOWER`             | 2      | Sécurité très faible |
| `LOW`               | 3      | Sécurité faible |
| `BELOW_NORMAL`      | 4      | En dessous de la moyenne |
| `NORMAL`            | 5      | Sécurité standard |
| `ABOVE_NORMAL`      | 6      | Sécurité légèrement renforcée |
| `HIGH`              | 7      | Haute sécurité |
| `HIGHER`            | 8      | Très haute sécurité |
| `HIGHEST`           | 9      | Niveau de sécurité maximal |

#### ✅ Exemple d’utilisation

```python
from secugenpy import secugen

resultat = secugen.comparaisonEmpreintesDigitales(template1, template2, "NORMAL")

print(resultat)
```

---
daspython 4.0.11
DAS api client.
Requires Python: >=3.11

# Welcome to the DAS Python package

The [Royal Netherlands Institute for Sea Research](https://www.nioz.nl) has its data management system to help scientists archive and access their data. This tool is called: **Data Archive System (DAS)** and this package is its Python client.

_*NOTE: This package requires Python 3.11 and plus*_

# To install using pip

```
    $ pip install daspython
```

# To install this package locally use the following commands:

## Create a virtual environment first:

### Install virtualenv if not installed.
```
    $ pip install virtualenv
```

### Create the virtual enviroment
```
    $ virtualenv .venv
```

### Activate your virtual environment (for Windows)
```
    $ .\.venv\Scripts\activate.ps1   
```

### Deactivate your virtual environment (for Windows)
```
    $ deactivate
```


### Now install the dependencies

```powershell
    $ pip install -r .\requirements.txt
```

The best way to see how each method is used is visiting out [automated test scripts](https://git.nioz.nl/ict-projects/das-python/-/tree/master/tests) page.

# Authentication

Use this class to authenticate and keep your token that will be needed to use with all other service classes.

##### Usage

```python
from daspython.auth.authenticate import DasAuth

auth = DasAuth('DAS url', 'Your user name', 'Your password')

if (auth.authenticate()):
    print('You are connected ...')    
```

# Unit Tests

```
python -m unittest tests/test_something.py
```

## Deploying this package:

```
$ pip install twine
```
### Install twine
```
twine upload dist/*
```
Than follow the instructions to publish/upload the distribution files to pypi.org
pybioimage 0.1.0
A small package for custom image analysis tasks.
Requires Python: >=3.12

# pybioimage

## Installation

To install ``pybioimage`` run the following command.

````
pip install pybioimage
````

## Usage

The package is divided into submodules for FRAP, vertex enrichment, and cell 
aggregation analysis. Each of these submodules exposes an ``Analyzer`` class,
which constitutes the main class to use. These classes are instantiated with a
path pointing to the respective image. In order to make 
that as simple as possible, there is a convenience function in the ``utils`` 
module called ``find_files()``. This function searches for and returns all 
files from a given path fulfilling certain requirements. You can use this to 
find all files that are supposed to be analyzed.

````python
from pybioimage.utils import find_files


# Find all TIFF files in the 'data' folder but ignores files and folders 
# starting with an underscore '_'. 
find_files("data", pattern=".*\\.tiff$", ignore="^_.*")
````

Using regular expression, you can further finetune which files to return and 
then use those to instantiate your analyzer.
ntsulib 1.2.231
ntsulib
Requires Python: >=3.11
Keywords: utility, library, python, ntsulib

# ntsulib

[![Python Version](https://img.shields.io/badge/python-3.11+-blue.svg)](https://python.org)
[![License](https://img.shields.io/badge/license-GPL-green.svg)](LICENSE)
[![Code Style](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)

`ntsulib` 是一个多功能工具库，提供异步处理、C 语言交互、加密、PostgreSQL、IO 操作等常用功能封装。

## 功能模块

| 模块               | 描述          | Logo |
|------------------|-------------|------|
| **nasyncthread** | 异步处理工具      | ![AsyncIO](https://img.shields.io/badge/-Asyncio-3776AB?logo=python&logoColor=white) |
| **c**            | C语言交互接口     | ![C](https://img.shields.io/badge/-C-A8B9CC?logo=c&logoColor=white) |
| **nencry**       | 加密解密工具      | ![Crypto](https://img.shields.io/badge/-Cryptography-6DB33F?logo=gnu&logoColor=white) |
| **ndatabase**    | 数据库操作封装     | ![PostgreSQL](https://img.shields.io/badge/-PostgreSQL-4169E1?logo=postgresql&logoColor=white) |
| **serverquery**  | 服务器查询工具     | ![Network](https://img.shields.io/badge/-Network-2496ED?logo=internet-explorer&logoColor=white) |
| **nlogger**      | 日志记录工具      | ![Logging](https://img.shields.io/badge/-Logging-000000?logo=loggly&logoColor=white) |

## 安装

```bash
pip install ntsulib
```

## 使用

```python
# 1
import ntsulib as nl
# 2
from ntsulib import *
```
opsramp-gcp-utils 1.0.2
OpsRamp Analytics SDK
Requires Python: >=3.6

## OpsRamp Analytics Utilities

This is the SDK for writing OpsRamp analytics apps. It is based on [dash](https://plotly.com/dash/), and it has a number of utility functions.

It contains [analysis wrapper project](https://github.com/opsramp/analysis-wrapper).

It is published on [Pypi](https://pypi.org/project/opsramp-analytics-utils/)

#### How to publish on Pypi

After make updates on SDK, modify the version in _setup.py_.

```
python setup.py sdist bdist_wheel
python -m twine upload dist/*

Note: if above command not works, then use below command
python -m twine upload --skip-existing dist/*
```

- To upgrade the sdk for your app
```
pip install --no-cache-dir --upgrade opsramp-analytics-utils
```