semvec/pypi-packages huggingface dataset:
dverse-nats-helper 0.2.0
A Python library for NATS server connection, publishing, creation, and validation of events.
Requires Python: >=3.8

# dverse-nats-helper

dverse-nats-helper is a Python library for dealing nats server connection, publishng, creation and validation of the events

## Installation

Use the package manager [pip](https://pypi.org/project/dverse-nats-helper/0.1.0/) to install dverse-nats-helper.

```bash
pip install dverse-nats-helper
```

## Usage

```python
from dverse_nats_helper.event_builder import build_event
from dverse_nats_helper.nats_connection import publish_event, connect_nats

# Establishes a connection to the NATS server.
await connect_nats("nats://nats:4222")

# Constructs an event message conforming to a predefined schema.
# The object must implement a to_event_object() method.
system = { "platform": "example", "service": "example_svc", "event_type": "created"},
new_event = build_event(product,actor={"actor_id": str(seller.id), "username": seller.username}, system)

# Publishes an event to the specified NATS subject
# after validating the data against a predefined schema.
await publish_event("example.subject", new_event)

```

## Contributing

Pull requests are welcome. For major changes, please open an issue first
to discuss what you would like to change.

Please make sure to update tests as appropriate.

## License

MIT License

Copyright (c) 2024 fuas-dverse

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights to
use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies
of the Software, and to permit persons to whom the Software is furnished to do
so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
mix-api 1.0.1
Mix Coin API Python3
Requires Python: >=3.7

# MixAPI

MixAPI — это асинхронная библиотека Python для взаимодействия с API сервиса Mix. Она предоставляет удобные методы для получения баланса, перевода средств и просмотра истории транзакций.

## Установка

Установите библиотеку с помощью pip:
```bash
pip install mix-api
```

## Пример использования
```python
import asyncio
from mix_api import MixAPI

async def main():
    token = "your_api_token_here"
    user_id = 12345
    
    async with MixAPI(token) as api:
        # Получить баланс пользователя
        balance = await api.get_balance(user_id)
        print("Баланс:", balance)

        # Перевести средства другому пользователю
        transfer_result = await api.transfer(to_id=67890, amount=100)
        print("Результат перевода:", transfer_result)

        # Получить историю транзакций
        transactions = await api.get_transactions(type_=1)
        print("История транзакций:", transactions)

asyncio.run(main())
```

## Методы

### `get_balance(user_id: int) -> Dict[str, Union[str, Dict]]`

Получает баланс указанного пользователя.

**Аргументы:**
- `user_id` (int): ID пользователя, чей баланс нужно получить.

**Возвращает:**
- Словарь с информацией о балансе или ошибкой.

---

### `transfer(to_id: int, amount: int) -> Dict[str, Union[str, Dict]]`

Переводит MIX средства другому пользователю.

**Аргументы:**
- `to_id` (int): ID получателя.
- `amount` (int): Количество MIX для перевода.

**Возвращает:**
- Словарь с результатом операции.

---

### `get_transactions(type_: int = 1) -> Dict[str, Union[str, List]]`

Получает историю транзакций.

**Аргументы:**
- `type_` (int, optional): Тип транзакций. Возможные значения:
  - `1`: Входящие транзакции (по умолчанию).
  - `2`: Исходящие транзакции.

**Возвращает:**
- Словарь со списком транзакций.

## Примечания
- Убедитесь, что вы используете валидный `token` для взаимодействия с API.
- Для работы требуется библиотека `aiohttp`. Она устанавливается автоматически с `mix-api`.
DigitalBrainSDK 0.0.2
A Python SDK for Digital Brain analysis
Requires Python: >=3.6

# DigitalBrainSDK

DigitalBrainSDK is a Python package designed to provide advanced data processing capabilities. 

## Installation

To install the DigitalBrainSDK, you can use pip:

```
pip install DigitalBrainSDK
```

## Usage

Here is a simple example of how to use the DigitalBrainSDK:

TODO

## Utilities

TODO

## Contributing

Contributions are welcome! Please feel free to submit a pull request or open an issue for any enhancements or bug fixes.

## License

This project is licensed under the MIT License. See the LICENSE file for more details.
scMO-LONMF 0.0.1
A non-negative matrix factorization model based on graph Laplacian and optimal transmission for Paired single-cell multi-omics data integration.
Requires Python: >=3.8

# LONMF_py
LONMF: A non-negative matrix factorization model based on graph Laplacian and optimal transmission for Paired single-cell multi-omics data integration

This is the Python implementation of the LONMF algorithm. Note that this implementation supports GPU acceleration.

## 1. Installation
You can use the following command to install LONMF:
```
pip install LONMF
```

## 2. Usage
LONMF accepts a muon object as input and populates the obsm and uns fields with embeddings and dictionaries, respectively.
The pre-processed 10X Multiome demo dataset is available for download here. It is recommended that a GPU be used to run the model.
To initialize and run the LONMF model, the following code should be used:

```
from LONMF.model import LONMF
import mudata as md
import scanpy as sc

# Load data into a Muon object.
mdata = md.read_h5mu("my_data.h5mu")

# Initialize and train the model.
model = LONMF(latent_dim=15)
model.train(mdata)

# Visualize the embedding with UMAP.
sc.pp.neighbors(mdata, use_rep="W_OT")
sc.tl.umap(mdata)
sc.pl.umap(mdata)
```

'LONMF' class also has other methods, you can use the 'help' or '?' command for more details explanations of the methods.