semvec/pypi-packages huggingface dataset:
imspy-predictors 0.5.1
ML-based predictors for CCS, retention time, and fragment intensity in mass spectrometry.
Requires Python: <3.14,>=3.11

# imspy-predictors

ML-based predictors for CCS, retention time, and fragment intensity in mass spectrometry.

## Installation

```bash
pip install imspy-predictors
```

For remote model access via Koina servers:

```bash
pip install imspy-predictors[koina]
```

## Features

- **CCS Prediction**: Deep learning models for collision cross section / ion mobility prediction
- **Retention Time Prediction**: GRU-based retention time predictors
- **Fragment Intensity Prediction**: Prosit 2023 timsTOF intensity predictor
- **Charge State Prediction**: Binomial and deep learning charge state distribution models
- **Koina Integration**: Access remote prediction models via [Koina](https://koina.wilhelmlab.org) servers (optional)

### Available Koina Remote Models

When using `pip install imspy-predictors[koina]`, remote models can be configured in TimSim's `[models]` TOML section:

| Task | Model Name |
|------|-----------|
| **RT** | `Deeplc_hela_hf`, `Chronologer_RT`, `AlphaPeptDeep_rt_generic`, `Prosit_2019_irt` |
| **CCS** | `AlphaPeptDeep_ccs_generic`, `IM2Deep` |
| **Intensity** | `prosit`, `alphapeptdeep`, `ms2pip` |

```toml
[models]
rt_model = "AlphaPeptDeep_rt_generic"
ccs_model = ""                          # "" = local PyTorch model
intensity_model = "prosit"
```

## Quick Start

```python
from imspy_predictors import (
    load_deep_ccs_predictor,
    load_deep_retention_time_predictor,
    Prosit2023TimsTofWrapper,
)

# Load CCS predictor
ccs_model = load_deep_ccs_predictor()

# Load RT predictor
rt_model = load_deep_retention_time_predictor()

# Load intensity predictor
intensity_model = Prosit2023TimsTofWrapper()
```

## Submodules

- **ccs/**: CCS / ion mobility prediction
- **rt/**: Retention time prediction
- **intensity/**: Fragment intensity prediction (Prosit)
- **ionization/**: Charge state distribution prediction
- **koina_models/**: Koina remote model access (requires `koinapy`)
- **utilities/**: Tokenizers for ML models

## Dependencies

- **imspy-core**: Core data structures (required)
- **PyTorch**: Deep learning framework (required)
- **koinapy**: Koina API client (optional, for remote models)

## Optional Dependencies

Some functionality requires additional packages:

- **imspy-search**: For PSM-based predictions using sagepy
- **imspy-simulation**: For simulation utilities (e.g., flatten_prosit_array)

## Related Packages

- **imspy-core**: Core data structures and timsTOF readers
- **imspy-search**: Database search functionality
- **imspy-simulation**: Simulation tools for timsTOF data
- **imspy-vis**: Visualization tools

## License

MIT License - see LICENSE file for details.
onemix-bot 1.0.0
Python SDK for building bots on the ONEMIX messenger platform
Requires Python: >=3.8
Keywords: onemix bot messenger api sdk

# onemix-bot — Python SDK для ботов ONEMIX

Библиотека для создания ботов на платформе ONEMIX мессенджера.

## Установка

```bash
pip install onemix-bot
```

Или из исходников:

```bash
git clone <repo>
cd onemix-bot-sdk
pip install -e .
```

## Быстрый старт

```python
from onemix_bot import OnemixBot

bot = OnemixBot(
    token="YOUR_BOT_TOKEN",        # Получите у @botfather
    api_url="https://your-server.com"  # URL вашего ONEMIX сервера
)

@bot.command("start")
def on_start(msg):
    bot.reply(msg, "Привет! Я бот ONEMIX")

@bot.on_message()
def echo(msg):
    bot.reply(msg, f"Вы написали: {msg['content']}")

bot.run()
```

## API

### OnemixBot(token, api_url, poll_interval=2.0)

Создаёт экземпляр бота.

### Методы

| Метод | Описание |
|-------|----------|
| `bot.get_me()` | Информация о боте |
| `bot.send_message(chat_id, text)` | Отправить сообщение |
| `bot.reply(msg, text)` | Ответить на сообщение |
| `bot.get_updates(limit=100)` | Получить обновления |
| `bot.set_webhook(url, secret=None)` | Установить вебхук |
| `bot.delete_webhook()` | Удалить вебхук |
| `bot.set_commands(commands)` | Установить команды |
| `bot.run()` | Запустить polling loop |
| `bot.run_threaded()` | Запустить в фоновом потоке |
| `bot.stop()` | Остановить polling |
| `bot.process_update(update)` | Обработать обновление (для вебхуков) |

### Декораторы

```python
@bot.command("start")         # Обработка команды /start
@bot.on_message()             # Обработка всех текстовых сообщений
@bot.on_message(pattern=r"") # Обработка по regex-шаблону
```

## Webhook-режим

```python
from flask import Flask, request
from onemix_bot import OnemixBot

app = Flask(__name__)
bot = OnemixBot(token="TOKEN", api_url="https://server.com")

@bot.command("start")
def start(msg):
    bot.reply(msg, "Привет!")

@app.route("/webhook", methods=["POST"])
def webhook():
    bot.process_update(request.json)
    return "ok"

if __name__ == "__main__":
    bot.set_webhook("https://your-server.com/webhook")
    app.run(port=5000)
```

## Лицензия

MIT
datamanagement 0.1.1
A flexible data container with query capabilities
Requires Python: >=3.7

# DataManagement
DataManagement - A flexible data container with query capabilities.  

This pure-python package provides DataPoint and DataSerials classes for managing collections of data with powerful filtering, aggregation, and serialization.

## Features

- **Dynamic DataPoints**: Create objects with arbitrary attributes
- **Powerful Filtering**: Django-like query syntax (`age__gte=18`, `name__contains='John'`)
- **Aggregation**: Group by and aggregate data like SQL
- **Multiple Serialization**: XML, JSON, Pickle, Pandas DataFrame
- **Method Chaining**: Build complex queries elegantly
- **Rich Statistics**: Built-in summary, describe, and info methods
- **HTML Reports**: Generate beautiful interactive reports

## Installation

```bash
pip install datamanagement
```

## Quick Start
```python
from datamanagement import DataPoint, DataSerials

# Create data points
ds = DataSerials()
ds.append(DataPoint(id='1', name='Alice', age=25, score=85.5))
ds.append(DataPoint(id='2', name='Bob', age=30, score=92.0))

# Filter data
adults = ds.filter(age__gte=18, score__gte=80)

# Group and aggregate
stats = ds.group_by('age_group', 
                    avg_score=('score', 'mean'),
                    count=('id', 'count'))

# Generate report
ds.to_html_report('report.html')
```

## Documentation
Full documentation available at [Document.md](https://github.com/CoderPower-Schenek/DataManagement/blob/main/Document.md).

## License
MIT License - see [LICENSE](https://github.com/CoderPower-Schenek/DataManagement/blob/main/LICENSE) file for details.
atomrdf 0.11.0
Ontology based structural manipulation and quering

# atomRDF

> [!NOTE]
> `atomRDF` was previously called `pyscal-rdf`. 

[![codecov](https://codecov.io/gh/pyscal/atomRDF/graph/badge.svg?token=X7S3TP2MP6)](https://codecov.io/gh/pyscal/atomRDF)
[![Conda Version](https://img.shields.io/conda/vn/conda-forge/atomrdf.svg)](https://anaconda.org/conda-forge/atomrdf)
![PyPI - Python Version](https://img.shields.io/pypi/pyversions/atomrdf?label=pypi&link=https%3A%2F%2Fpypi.org%2Fproject%2Fatomrdf%2F)
[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.10973374.svg)](https://doi.org/10.5281/zenodo.10973374)

`atomRDF` is a python tool for ontology-based creation, manipulation, and quering of structures. `atomRDF` uses the [Computational Material Sample Ontology (CMSO)](https://github.com/Materials-Data-Science-and-Informatics/cmso-ontology). 

The package is currently under activate development and could be  unstable .

More details coming soon...


## Acknowledgements
This work is supported by the [NFDI-Matwerk](https://nfdi-matwerk.de/) consortia.

Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under the National Research Data Infrastructure – NFDI 38/1 – project number 460247524