semvec/pypi-packages huggingface dataset:
mariqt 1.0.1
The MareHub & Marine Imaging Community Image QA/QC and curation toolbox

We strive to make marine image data FAIR. We develop FDOs for images to establish a common language for marine imagery, we develop best-practice operating procedures for handling marine images and we develop the MarIQT software to apply the iFDOs and procedures to marine imagery. MarIQT stands for Image Quality control / quality assurance and curation Tools (IQT) conceptualised and developed by the MareHub working group on Videos/Images (part of the DataHub, a research data manangement initiative by the Helmholtz association).
dify-file-parser 1.0.0
基于 Dify 项目文件处理功能抽离的通用文件解析器
Requires Python: >=3.8

# 文件解析器 (File Parser)

一个基于 Dify 项目文件处理功能抽离的通用文件解析器，支持多种文件格式转换为 AI 可识别的文本。

## 功能特性

- 📄 支持多种文件格式：PDF、Word、Excel、PowerPoint、图片、文本等
- 🔍 集成 OCR 技术，支持图片文字识别
- 🚀 高性能异步处理
- 🔧 模块化设计，易于扩展
- 📝 统一的文本输出格式
- 🛡️ 错误处理和日志记录

## 支持的文件格式

| 格式 | 扩展名 | 状态 | 说明 |
|------|--------|------|------|
| PDF | .pdf | ✅ | 支持文本提取和 OCR |
| Word | .docx, .doc | ✅ | 支持文档内容提取 |
| Excel | .xlsx, .xls | ✅ | 支持表格数据提取 |
| PowerPoint | .pptx, .ppt | ✅ | 支持幻灯片内容提取 |
| 图片 | .jpg, .jpeg, .png, .bmp, .tiff | ✅ | 支持 OCR 文字识别 |
| 文本 | .txt, .md | ✅ | 直接文本读取 |
| CSV | .csv | ✅ | 表格数据解析 |

## 快速开始

### 安装依赖

```bash
pip install -r requirements.txt
```

### 基本使用

```python
from file_parser import FileParser

# 创建解析器实例
parser = FileParser()

# 解析单个文件
result = await parser.parse_file("document.pdf")
print(result.text)

# 批量解析文件
files = ["doc1.pdf", "doc2.docx", "image.png"]
results = await parser.parse_files(files)
for result in results:
    print(f"文件: {result.filename}")
    print(f"内容: {result.text[:100]}...")
```

## 项目结构

```
file-parser/
├── file_parser/           # 核心模块
│   ├── __init__.py
│   ├── base.py           # 基础解析器类
│   ├── parsers/          # 具体解析器实现
│   │   ├── __init__.py
│   │   ├── pdf_parser.py
│   │   ├── word_parser.py
│   │   ├── excel_parser.py
│   │   ├── ppt_parser.py
│   │   ├── image_parser.py
│   │   └── text_parser.py
│   ├── ocr/              # OCR 相关功能
│   │   ├── __init__.py
│   │   └── tesseract_ocr.py
│   └── utils/            # 工具函数
│       ├── __init__.py
│       └── file_utils.py
├── tests/                # 测试文件
├── examples/             # 示例代码
├── requirements.txt      # 依赖列表
└── README.md
```

## 许可证

MIT License
evlos-tools 0.1.0
Add your description here
Requires Python: <3.12,>=3.11

# Evlos-Tools

### Common config
1. trt: compile_mode: [quick_compile / quick_inference]
    * Use quick_compile for testing and use quick_inference for latency check and production

### exposure_fusion
Refer exposure_fusion README.md for usage

### template_matcher
Refer template_matcher README.md for usage
fennomix-mhc 0.0.1.dev0
FennOmix-MHC
Requires Python: <3.12,>=3.10
Keywords: contrastive learning, protein language model, immunopeptidomics, mass spectrometry, HLA, MHC, peptide-MHC binding prediction

# FennOmix-MHC

Foundation model for MHC class I peptide binding prediction built on deep contrastive learning.

See the [online documentation](https://fennomix.mhc.readthedocs.io/en/latest) for
full API details and tutorials.

## Installation

Install the latest release from PyPI:

```bash
pip install fennomix-mhc
```

Or install the development version directly from GitHub:

```bash
pip install git+https://github.com/FennOmix/FennOmix.MHC.git
```

## Command line interface

After installation the `fennomix-mhc` command exposes several sub-commands.  The examples below assume your peptide or protein sequences are stored in FASTA or tabular files.

### Embed MHC proteins

```bash
fennomix-mhc embed-proteins --fasta my_hla.fasta --out-folder ./output
```

### Embed peptides

```bash
fennomix-mhc embed-peptides --peptide-file peptides.tsv --out-folder ./output
```

### Predict epitopes for MHC alleles

```bash
fennomix-mhc predict-epitopes-for-mhc --peptide-file peptides.tsv \
    --alleles A02_01,B07_02 --out-folder ./output
```

### Predict MHC binders for given epitopes

```bash
fennomix-mhc predict-mhc-binders-for-epitopes --peptide-file peptides.tsv \
    --out-folder ./output
```

Additional commands `deconvolute-peptides` and `deconvolute-and-predict-peptides` are also available.

## Pipeline API

All functionality of the command line interface is available through the `fennomix_mhc.pipeline_api` module:

```python
from fennomix_mhc.pipeline_api import (
    embed_proteins,
    embed_peptides_from_file,
    predict_epitopes_for_mhc,
    predict_mhc_binders_for_epitopes,
)

# compute and save embeddings
embed_proteins("my_hla.fasta", "./output")
embed_peptides_from_file("peptides.tsv", "./output")

# run predictions using the saved files
predict_epitopes_for_mhc(
    "peptides.tsv",
    ["A02_01"],
    "./output",
)
```
cosmotech-run-orchestrator 2.0.2
Orchestration suite for Cosmotech Run Templates

# cosmotech-run-orchestrator

This library contains the `csm-orc` cli developed by [Cosmo Tech](https://cosmotech.com/) for its Digital
Twin platform.

If you are interested in using it, please contact [Cosmo Tech](https://cosmotech.com/)

## Installation

Documentation on the installation is available on
our [GitHub Page](https://cosmo-tech.github.io/run-orchestrator/)

Once installed you will have access to the `csm-orc` CLI and all its commands.

## Licence

> Copyright - 2023 - 2024 - Cosmo Tech
> 
> You are authorized to use this Cosmo Tech software following the execution of a specific agreement with Cosmo Tech or with an authorized licensee of Cosmo Tech.
> 
> Accordingly you should refer to this specific agreement for the purpose of the extent of the use that is permitted. This use shall mainly consist in using, displaying and executing the Cosmo Tech software for your own needs.
> 
> Except otherwise provided in the above-mentioned specific agreement:
> - you are not allowed to use this Cosmo Tech software for commercial purpose or to redistribute it without prior authorization from Cosmo Tech;
> - you are not allowed to copy, duplicate, modify, adapt and translate this Cosmo Tech software except as authorized under section L122-6-1 of the French Intellectual Property Code or specifically under the specific agreement with Cosmo Tech;
> - you must not remove this notice or any mention related to the copyright of Cosmo Tech;
> 
> This Cosmo Tech software is provided 'as is', without any express or implied warranty towards the users.
> 
> Any software generated through the use of this Cosmo Tech software shall be your own property to the extent this software does not contain any part of Cosmo Tech software source code.
pyHIARD 1.2.3
Development Status :: 4 - Beta
Requires Python: >=3.6

pyHIARD
=====

The Python HI Artificial and Real Database (pyHIARD ) is a project to create a comprehensive database of Artificial and real galaxies that can be used for testing the various  software that is available for fitting galaxy survey. In principle it is developed for testing Tilted Ring Fitting Software but in principle it can be used for testing any Software for which the input parameters should be known.


pyHIARD Requires working versions of tirific and sofia2  for full functionality.

CASA is required for full simulations which are achieved through the casa python modules and they should be installed through the pip install. Note however that currently the modules are only available up to python 3.8.
!!!!!!!!For python versions > 3.8 the install will progress normally but the casatasks will not be installed and the casa corruption method will be unavailable!!!!!!!!

tirific is required for the Artificial branch and can be installed trough kern-suite https://kernsuite.info/ for ubuntu or it can be downloaded from https://github.com/gigjozsa/tirific. If you are struggling with tirific's pg plot dependency the branch no_pgp can be installed from the github. It wll work with pyHIARD

SoFiA2 is required for the masking and can be downloaded from https://github.com/SoFiA-Admin/SoFiA-2

Except for The Real Observed Catalogue should work without any external installations as long as no new galaxies are added. Do note that at the first instance of creating a base galaxy it requires an internet connection to download the original cubes. An origins disclaimer will be positions in each directory.

Please see the read the docs pages for additional information and set up.


Installation
----

Download the source code from the Github or simply install with pip as:

  `pip install pyHIARD`

This should also install all required python dependencies. We recommend the use of python virtual environments. If so desired a pyHIARD installation would look like:

  `python3 -m venv pyHIARD_venv`

  `source pyHIARD_venv/bin/activate.csh`

  `pip install --upgrade wheel setuptools pip`

  `pip install pyHIARD`

(In case of bash the correct middle line is 	source pyHIARD_venv/bin/activate)
You might have to restart the env:

  `deactivate`

  `source pyHIARD_venv/bin/activate.csh`

Often it is necessary to update the casa tables through:

  `python -m casatools --update-user-data`


Running pyHIARD
----

cd to the directory where you want the database to exist.
run pyHIARD by typing:

  `pyHIARD configuration_file=my_setup.yml`

or

  `pyHIARD`

answer the questions about what you want to create.

Setting can also be specified from the command line. These will take preference over the configuration file. For example if you want to recreate your data base with a slightly different beam. you could rerun pyHIARD as:

  'pyHIARD configuration_file=pyHIARD_tests.yml "agc.variables_to_vary=[beam_size]" "agc.beam_size=[[7.5,7.5,30.]]"