semvec/pypi-packages huggingface dataset:
image-processing-package-master 0.0.1
Image Processing Package using skimage
Requires Python: >=3.5

# image_processing

Description. 
The package image_processing is used to:
	Processing:
		- Histogram matching
		- Structural Similarity
		- Resize Image
	Utils:
		- Read Image
		- Save Image
		- Plot Image
		- Plot Result
		- Plot Histogram

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install package_name

```bash
pip install image_processing
```

## Usage

```python
from package_name.module1_name import file1_name
file1_name.my_function()
```

## Author
Pedro

## License
[MIT](https://choosealicense.com/licenses/mit/)
mdkit-pcl 0.1.3
A molecular dynamics simulation toolkit
Requires Python: >=3.6

# MDKIT - Molecular Dynamics Toolkit

A toolkit for molecular dynamics simulation preprocessing and postprocessing.

## Features

- Build simulation boxes with Packmol
- Generate Gromacs topology files
- Generate Gromacs parameter files (em.mdp, npt.mdp)

## Installation

```bash
pip install mdkit-pcl
string-repetition 0.3.2
一个高效的字符串重复检测算法
Requires Python: >=3.6

# String Repetition Detector

一个基于后缀自动机的高效字符串重复检测工具，可以找出字符串中符合指定长度和重复次数的重复子串。

## 安装方法

### 从源码安装

```bash
# 克隆仓库
git clone https://github.com/your-username/string_repetition.git
cd string_repetition

# 安装到当前Python环境
pip install -e .
```

### 直接从PyPI安装

```bash
pip install string_repetition
```

## 基本用法

```python
from string_repetition import StringRepetitionDetector

# 创建检测器实例
detector = StringRepetitionDetector(min_length=3, min_repeats=2)

# 检测单个字符串
text = "abcabcabcabc"
result = detector.detect(text)

if result.has_repetition:
    print(f"发现重复: '{result.substring}' 重复了 {result.repetition_count} 次")
    print(f"位置: {result.start_pos}-{result.end_pos}")
else:
    print("没有发现符合条件的重复")

# 批量检测多个字符串
texts = ["abcabc", "defghi", "xyzxyzxyz"]
results = detector.detect_batch(texts)
for text, result in zip(texts, results):
    print(f"文本 '{text}': {'有重复' if result.has_repetition else '无重复'}")
```

## 高级用法

```python
# 并行处理长文本
long_text = "abc" * 1000000
result = detector.detect_string(long_text, parallel=True)

# 自定义参数
custom_detector = StringRepetitionDetector(
    min_length=5,    # 最小重复子串长度
    min_repeats=3    # 最小重复次数
)
```

## 运行测试

```bash
# 从项目根目录运行
python -m unittest discover tests

# 或者使用pytest
pytest
```
bar-tender-api 1.0.0
BarTender 接口封装
Requires Python: <3.14,>=3.11

# 通过 BarTender 来控制打印机

## 1. 安装打印机
### 1.1 下载打印机驱动
### 1.2 如果需要升级打印机固件, 则升级固件

## 2. 安装 BarTender
### 1.1 安装的时候下载 SDK

## 3. 在 BarTender 上设计模板

## 4. 通过 bar_tender_pai 接口调用模板即可
dbt-iomete 1.7.9
The Apache Spark (IOMETE) adapter plugin for dbt
Requires Python: >=3.7

<p align="center">
  <img src="docs/iomete-logo.png" alt="iomete logo" width="300" /> <img src="https://raw.githubusercontent.com/dbt-labs/dbt/ec7dee39f793aa4f7dd3dae37282cc87664813e4/etc/dbt-logo-full.svg" alt="dbt logo" width="250"/>
</p>
<p align="center">
  <a href="https://github.com/iomete/dbt-iomete/actions/workflows/main.yml">
    <img src="https://github.com/iomete/dbt-iomete/actions/workflows/main.yml/badge.svg?event=push" alt="Unit Tests Badge"/>
  </a>
  <a href="https://github.com/iomete/dbt-iomete/actions/workflows/release.yml">
    <img src="https://github.com/iomete/dbt-iomete/actions/workflows/release.yml/badge.svg?event=push" alt="Release Badge"/>
  </a>
</p>

**[dbt](https://www.getdbt.com/)** enables data analysts and engineers to transform their data using the same practices that software engineers use to build applications.

dbt is the T in ELT. Organize, cleanse, denormalize, filter, rename, and pre-aggregate the raw data in your warehouse so that it's ready for analysis.

## dbt-iomete

The `dbt-iomete` package contains all the code enabling dbt to work with iomete.

This adapter is forked from the [dbt-spark](https://github.com/dbt-labs/dbt-spark)

## Getting started

### Installation

```shell
pip install dbt-iomete
```

Alternatively, you can install the package from GitHub with:

```shell
pip install git+https://github.com/iomete/dbt-iomete.git
```

### Profile Setup

```yaml
iomete:
  target: dev
  outputs:
    dev:
      type: iomete
      host: <host>
      port: 443
      https: true # or http
      dataplane: <iomete_dataplane>
      domain: <iomete_domain>
      lakehouse: <serverless_lakehouse_name>
      catalog: <catalog_name>
      schema: <database_name>
      user: "{{ env_var('DBT_IOMETE_USER_NAME') }}"
      token: "{{ env_var('DBT_IOMETE_TOKEN') }}"
```

For more information, consult [the docs](https://iomete.com/docs/guides/dbt/getting-started-with-iomete-dbt).
gpt-batch 0.1.9
A package for batch processing with OpenAI API.


```markdown
# GPT Batcher

A simple tool to batch process messages using OpenAI's GPT models. `GPTBatcher` allows for efficient handling of multiple requests simultaneously, ensuring quick responses and robust error management.

## Installation

To get started with `GPTBatcher`, clone this repository to your local machine. Navigate to the repository directory and install the required dependencies (if any) by running:

```bash
pip install gpt_batch
```

## Quick Start

To use `GPTBatcher`, you need to instantiate it with your OpenAI API key and the model name you wish to use. Here's a quick guide:

### Handling Message Lists

This example demonstrates how to send a list of questions and receive answers:

```python
from gpt_batch.batcher import GPTBatcher

# Initialize the batcher
batcher = GPTBatcher(api_key='your_key_here', model_name='gpt-3.5-turbo-1106')

# Send a list of messages and receive answers
result = batcher.handle_message_list(['question_1', 'question_2', 'question_3', 'question_4'])
print(result)
# Expected output: ["answer_1", "answer_2", "answer_3", "answer_4"]
```

### Handling Embedding Lists

This example shows how to get embeddings for a list of strings:

```python
from gpt_batch.batcher import GPTBatcher

# Reinitialize the batcher for embeddings
batcher = GPTBatcher(api_key='your_key_here', model_name='text-embedding-3-small')

# Send a list of strings and get their embeddings
result = batcher.handle_embedding_list(['question_1', 'question_2', 'question_3', 'question_4'])
print(result)
# Expected output: ["embedding_1", "embedding_2", "embedding_3", "embedding_4"]
```

### Handling Message Lists with different API

This example demonstrates how to send a list of questions and receive answers with different api:

```python
from gpt_batch.batcher import GPTBatcher

# Initialize the batcher
batcher = GPTBatcher(api_key='sk-', model_name='deepseek-chat',api_base_url="https://api.deepseek.com/v1")


# Send a list of messages and receive answers
result = batcher.handle_message_list(['question_1', 'question_2', 'question_3', 'question_4'])

# Expected output: ["answer_1", "answer_2", "answer_3", "answer_4"]
```
## Configuration

The `GPTBatcher` class can be customized with several parameters to adjust its performance and behavior:

- **api_key** (str): Your OpenAI API key.
- **model_name** (str): Identifier for the GPT model version you want to use, default is 'gpt-3.5-turbo-1106'.
- **system_prompt** (str): Initial text or question to seed the model, default is empty.
- **temperature** (float): Adjusts the creativity of the responses, default is 1.
- **num_workers** (int): Number of parallel workers for request handling, default is 64.
- **timeout_duration** (int): Timeout for API responses in seconds, default is 60.
- **retry_attempts** (int): How many times to retry a failed request, default is 2.
- **miss_index** (list): Tracks indices of requests that failed to process correctly.

For more detailed documentation on the parameters and methods, refer to the class docstring.
doc-page-extractor-test 0.2.3
doc page extractor can identify text and format in images and return structured data.

# doc page extractor

English | [中文](./README_zh-CN.md)

## Introduction

doc page extractor can identify text and format in images and return structured data.

## Installation

```shell
pip install doc-page-extractor
```

```shell
pip install onnxruntime==1.21.0
```

## Using CUDA

Please refer to the introduction of [PyTorch](https://pytorch.org/get-started/locally/) and select the appropriate command to install according to your operating system.

In addition, replace the command to install `onnxruntime` in the previous article with the following:

```shell
pip install onnxruntime-gpu==1.21.0
```

## Example

```python
from PIL import Image
from doc_page_extractor import DocExtractor

extractor = DocExtractor(
  model_dir_path=model_path, # Folder address where AI model is downloaded and installed
  device="cpu", # If you want to use CUDA, please change to device="cuda".
)
with Image.open("/path/to/your/image.png") as image:
  result = extractor.extract(
  image=image,
  lang="ch", # Language of image text
)
for layout in result.layouts:
  for fragment in layout.fragments:
    print(fragment.rect, fragment.text)
```

## Acknowledgements

The code of `doc_page_extractor/onnxocr` in this repo comes from [OnnxOCR](https://github.com/jingsongliujing/OnnxOCR).

- [DocLayout-YOLO](https://github.com/opendatalab/DocLayout-YOLO)
- [OnnxOCR](https://github.com/jingsongliujing/OnnxOCR)
- [layoutreader](https://github.com/ppaanngggg/layoutreader)
- [StructEqTable](https://github.com/Alpha-Innovator/StructEqTable-Deploy)
- [LaTeX-OCR](https://github.com/lukas-blecher/LaTeX-OCR)
quantumgeometrytensor 0.2.4
This is a package using calculate the quantum geometry including Zeeman QGT, S_QGT as well as the corresponding dipole quantities
Requires Python: >=3.7

# QuantumGeometryTensor
This is a package that is used to calculate the quantum geometry tensor(QGT), Zeeman QGT and S-QGT, as well as the corresponding dipole quantities