semvec/pypi-packages huggingface dataset:
particlespy 0.6.3
A package to perform particle segmentation and analysis

[![License: GPL v3](https://img.shields.io/badge/License-GPLv3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0)
![Build Status](https://github.com/ePSIC-DLS/particlespy/actions/workflows/python-package-conda.yml/badge.svg)
[![codecov](https://codecov.io/gh/ePSIC-DLS/ParticleSpy/branch/master/graph/badge.svg)](https://codecov.io/gh/ePSIC-DLS/ParticleSpy)
[![PyPI version](https://badge.fury.io/py/particlespy.svg)](https://badge.fury.io/py/particlespy)
[![DOI](https://zenodo.org/badge/154111577.svg)](https://zenodo.org/badge/latestdoi/154111577)

# ParticleSpy
Package for analysing particles in electron microscopy data sets.

The package can be installed from PyPI using pip install ParticleSpy.

Documentation can be found at https://epsic-dls.github.io/particlespy/index.html.
opensymbiose 0.0.4
OpenSymbiose is an open-source biotechnology / biology research AI agent designed to support researcher.
Requires Python: >=3.10

---
title: opensymbiose
app_file: src/opensymbiose/gradio/app.py
sdk: gradio
sdk_version: 5.31.0
python_version: 3.13
---
# OpenSymbiose: Open Source Biotechnology AI Agent

OpenSymbiose is an open-source biotechnology / biology research AI agent designed to support researchers.

[DOCUMENTATION](https://lambda-science.github.io/OpenSymbiose/) - [DEMO ON HUGGINGFACE](https://huggingface.co/spaces/corentinm7/opensymbiose) - [CODE REPO](https://github.com/lambda-science/OpenSymbiose)

Creator and Maintainer: [**Corentin Meyer**, PhD](https://cmeyer.fr/) - <contact@cmeyer.fr>

## Installation

If you want just to interact with it, an online demo is available
on [HuggingFace](https://huggingface.co/spaces/corentinm7/opensymbiose)

**Note:** In every-case you will need an environment variable or a .env file with your `MISTRAL_API_KEY` secret.

- Install from PyPi: `pip install opensymbiose`
    - Locally: Run in your terminal `opensymbiose` it will boot the gradio app
    - In Docker: `make dockerbuild && make dockerrun` to run in Docker.
- From GitHub: clone the repository and run `make dev`. It will launch the Gradio app with hot-reloading.
metamorphic-multifunction-search 4.2.0
Comprehensive Python Module for Protein Data Management: Designed for streamlined integration and processing of protein information from both UniProt and PDB. Equipped with features for concurrent data fetching, robust error handling, and database synchronization.
Requires Python: <4.0,>=3.10

[![PyPI - Version](https://img.shields.io/pypi/v/metamorphic_multifunction_search)](https://pypi.org/project/metamorphic_multifunction_search/)
[![Documentation Status](https://readthedocs.org/metamorphic_multifunction_search/badge/?version=latest)](https://metamorphic_multifunction_search.readthedocs.io/en/latest/?badge=latest)
![Linting Status](https://github.com/CBBIO/metamorphic_multifunction_search/actions/workflows/test-lint.yml/badge.svg?branch=main)

# **Metamorphic & Multifunctional Protein Search**

## 🔬 Overview

**`metamorphic_multifunction_search`** is a systematic protocol for the **large-scale detection of structural metamorphisms and protein multifunctionality**, built on top of the Protein Information System (PIS).

The project combines structural alignments, functional GO annotations, and protein language models to uncover hidden relationships between structure and function across model and non-model organisms.

---

## 🧠 What Does This Protocol Do?

### 1. **Structural Metamorphism Detection**

* Aligns 3D protein structures with high sequence identity.
* Detects divergent conformations (i.e. metamorphisms) using metrics like RMSD or FC-score.
* Uses large-scale filtering (e.g., CD-HIT) and pairwise structural comparison.

### 2. **Functional Multifunctionality Analysis**

* Extracts Gene Ontology (GO) annotations per protein.
* Computes semantic distances between GO terms within each namespace (MF, BP, CC).
* Identifies the most divergent pair of terms per protein to quantify multifunctionality.

---

## ⚙️ Requirements

* Python 3.11.6
* RabbitMQ
* PostgreSQL with `pgvector` extension
* Docker (optional but recommended for deployment)

---

## 🚀 Quick Start

1. **Start PostgreSQL with `pgvector`:**

```bash
docker run -d --name pgvectorsql \
    -e POSTGRES_USER=user \
    -e POSTGRES_PASSWORD=password \
    -e POSTGRES_DB=BioData \
    -p 5432:5432 \
    pgvector/pgvector:pg16
```

2. **Start RabbitMQ:**

```bash
docker run -d --name rabbitmq \
    -p 15672:15672 \
    -p 5672:5672 \
    rabbitmq:management
```

3. **Run the main protocol:**

```bash
python main.py
```

This command executes the full pipeline: data extraction, structural filtering, alignment, functional analysis, and metric computation.

---

## ⚒️ Customization

You can tailor the pipeline by editing the `config.yaml` file or modifying `main.py` to:

* Switch embedding models
* Apply taxonomy-based filters
* Add new annotation types or similarity metrics

---

## 📚 Related Projects

* 🔗 [Protein Information System (PIS)](https://github.com/CBBIO/ProteinInformationSystem)
* 🔗 [FANTASIA: Functional Annotation Toolkit](https://github.com/CBBIO/FANTASIA)

---

Let me know if you'd like me to overwrite your current `README.md` with this version.
vnp 0.1.2
Standard PIP Package for VNP integration apps.
Requires Python: >=3.12

# VNP Pip Package

## Overview

`vnp-pip-package` (or `vnp` when imported in Python) is a utility library designed to provide common functionalities and standardized components for VNP's Python projects. It aims to reduce code duplication and promote best practices across different projects.

## Modules

The package currently includes the following modules:

- **bigquery.py**:

  - Provides the `BigQueryController` class for simplified interaction with Google BigQuery.
  - Supports creating tables, checking table existence, loading data from JSON, loading data from Google Cloud Storage (GCS), and executing queries.
  - Offers methods for schema inference from JSON data and efficient data loading via GCS staging.

- **logger.py**:

  - Provides the `get_logger` function for obtaining configured logger instances.
  - Standardizes logging format and setup across projects.
  - Configures both root logger and common third-party library loggers to ensure consistent logging behavior.

- **secret_manager.py**

  - Provides the `get_secret` function for retrieving secrets from Google Secret Manager.
  - Enables to get, create, delete and lists the secrets.

- **storage.py**:
  - Provides the `StorageManager` class for managing Google Cloud Storage operations.
  - Supports uploading data (dictionaries, lists of dictionaries, Pandas DataFrames) to GCS as JSON files.
  - Includes functionality for deleting files from GCS.

## Building and Publishing

To build and publish the package, follow these steps:

1. Install build dependencies:

   ```bash
   uv add build twine
   ```

2. Build the package:

   ```bash
   uv build
   ```

3. Publish the package:

   ```bash
   uv run twine upload --verbose dist/*
   ```

## Usage

After installation, you can import and use the modules and classes provided by `vnp_utils` in your Python projects.

**Example (using BigQueryController):**

```python
from vnp_utils.bigquery import BigQueryController

# ... (Assume bigquery_payload and key_path are defined) ...

bq_controller = BigQueryController(bigquery_payload=bigquery_payload, key_path=key_path)

# Example: Export JSON data to BigQuery via GCS
gcs_uri = bq_controller.export_to_bq_via_gcs(json_data=data)
print(f"Data loaded to BigQuery via GCS: {gcs_uri}")
```

Refer to the individual module files (`bigquery.py`, `logger.py`, `storage.py`) for detailed class and function documentation and usage examples.
licelfile 0.1.3
Package that reads and writes licel lidar files.

## 📊 LicelFile 📋

### Описание проекта

Данный проект реализует средства для чтения и обработки специализированных научных файлов формата Licel (transient recorder), используемых в лазерных измерительных приборах (лидарах). Библиотека разработана на языке Python и предоставляет высокоуровневый интерфейс для удобного доступа к данным измерений, хранящимся в таких файлах.

### Особенности библиотеки:

- Возможность прямого чтения данных из бинарных файлов формата Licel.
- Гибкий API для удобной навигации и анализа полученных данных.
- Поддерживает коллективную работу с несколькими файлами посредством сжатия в архивы формата ZIP.
- Эффективная обработка больших объемов данных с минимальными накладными расходами ресурсов.

### 🏷️ Ключевая терминология

- **Licel File**: Файл, содержащий измерения, выполненные прибором-лидаром.
- **Dataset**: Набор данных в рамках одного измерения, характеризующийся параметрами прибора и условиями эксперимента.
- **Profile**: Информация о конкретном канале измерения (частота лазера, ширина канала, разрешение и др.)

### 💻 Требования к среде

- Python >= 3.7
- Необходимые библиотеки:
  - `dataclasses`
  - `numpy`
  - `zipfile`
  - `datetime`
  - `struct`

### 💥 Основные классы и методы

- **`LicelProfile`**: Представляет профиль, связанный с конкретным каналом измерения (лазером).
- **`LicelFile`**: Основной класс, инкапсулирующий всю информацию, прочитанную из файла Licel.
- **`LicelFilePack`**: Класс, представляющий состав zip архива измерений.
- **`load_licel_file`**: Метод для загрузки и чтения одиночного файла Licel.
- **`load_licelfile_pack`**: Метод для массовой загрузки файлов из архива ZIP.

### 🚀 Быстрый старт

1. **Установка библиотеки**:
   Проект распространяется как самостоятельный модуль, доступный через PIP:

   ```bash
   pip install licelfile
   ```

2. **Базовый сценарий использования**:

   ```python
   from licelfile import load_licel_file

   # Чтение одиночного файла
   licel_file = load_licel_file('path/to/your/file.lic')

   # Доступ к данным
   datasets = licel_file.profiles
   for ds in datasets:
       print(ds.wavelength)
   ```

3. **Работа с архивами ZIP**:

   ```python
   from licelfile import load_licelfile_pack

   # Чтение группы файлов из ZIP
   licel_pack = load_licelfile_pack('path/to/your/data.zip')

   # Проходим по каждому файлу
   for filename, licel_file in licel_pack.files.items():
       print(f"Файл: {filename}, начало измерения: {licel_file.measurementStartTime}")
   ```

### 🩺 Тестирование

Проект сопровождается автоматизированными тестами, расположенными в директории `/tests`. Запуск тестов возможен следующей командой:

```bash
python -m unittest discover tests
```

### 🔮 Будущие планы

- Экспорт результатов в распространенные форматы (CSV, Excel, HDF5).
- Адаптация библиотеки для параллельной обработки больших объемов данных.
alens.pricedl 0.5.0
Commodity price retrieval, storage, and export in format compatible with Plain-Text Accounting applications.
Requires Python: >=3.12

# as-price-download
Price-database utility converted to Python

This is a rewrite of Pricedb-Rust utility. Pricedb-Rust originally came from Price-Database, which was in Python and was storing the prices in a SQLite database.
In later versions of Price-Database, the database was replaced with an in-memory store, outputting the final result into a Ledger prices text file.
In this project, the idea remains to fetch the prices, sort them by time, and output them into a Ledger prices text file.

# Install
```sh
uv tool install as-price-download
```

# Run
Example configuration file (.config/pricedb/pricedl.toml):
```
price_database_path = ":memory:"
alphavantage_api_key = ""
prices_path = "/home/prices.txt"
symbols_path = "/home/symbols.csv"
```


# Development
```sh
uv run python main.py [dl]
```

# Use with `bean-price`

The Vanguard downloader has been adapted for use with `bean-price` utility.
Use:

```sh
bean-price -e "AUD:pricedl.beanprice.vanguard_au_detail/HY"
```