semvec/pypi-packages huggingface dataset:
msdatasets 0.1.0
A unified dataset framework for mass spectrometry
Requires Python: >=3.10

# msdatasets

[![CI](https://github.com/chrisagrams/msdatasets/actions/workflows/ci.yml/badge.svg)](https://github.com/chrisagrams/msdatasets/actions/workflows/ci.yml)
[![codecov](https://codecov.io/gh/chrisagrams/msdatasets/graph/badge.svg)](https://codecov.io/gh/chrisagrams/msdatasets)
[![PyPI version](https://badge.fury.io/py/msdatasets.svg)](https://pypi.org/project/msdatasets/)

A unified dataset framework for mass spectrometry.

## Installation

```bash
pip install msdatasets
```

## Development

Install in development mode with test dependencies:

```bash
uv sync --extra dev
```

Set up pre-commit hooks:

```bash
uv run pre-commit install
```

Run tests:

```bash
uv run pytest
```

## License

MIT
kim-tools 0.4.6
Base classes and helper routines for writing KIM Tests
Requires Python: >=3.8
Keywords: kim-tools, kim_tools, openkim, crystal genome

# kim-tools

[![Testing](https://github.com/openkim/kim-tools/actions/workflows/testing.yml/badge.svg)](https://github.com/openkim/kim-tools/actions/workflows/testing.yml)
[![docs](https://app.readthedocs.org/projects/kim-tools/badge/?version=latest)](https://kim-tools.readthedocs.io/en/latest/)
[![PyPI](https://img.shields.io/pypi/v/kim-tools.svg)](https://pypi.org/project/kim-tools/)
[![codecov](https://codecov.io/gh/openkim/kim-tools/graph/badge.svg?token=G57VDZYY0F)](https://codecov.io/gh/openkim/kim-tools)

KIMTestDriver and SingleCrystalTestDriver classes for creating OpenKIM Test Drivers, and helper routines for writing
KIM Tests and Verification Checks. Documentation at https://kim-tools.readthedocs.io.

## Contributing Guide (Under Construction)

All contributed functions, classes and methods should be documented with Google style docstrings (https://google.github.io/styleguide/pyguide.html#383-functions-and-methods) and should have type hints for all arguments and return values (https://docs.python.org/3/library/typing.html). The docstrings are automatically rendered into the API documentation. To check that this is working correctly, install the Python packages in `docs/requirements-docs.txt` and run `make html` in `docs/`. There are some warnings, but there should be no errors, and the rendered docstring in the appropriate module html (e.g., `docs/build/html/kim_tools.ase.html`) should have a properly rendered section for the functions, classes and methods you wrote. See `kim_tools/test_driver/core.py` for a variety of examples of docstrings.

The code has a simple test suite using [pytest](https://docs.pytest.org/en/stable/). See the various files named `test_*` in `tests/` for examples and add tests for any code you write.

To confirm that your code will pass the linter and style checks, install pre-commit as in this guide: https://pre-commit.com/#quick-start. The checks will run every time you make a commit.

`kim_tools/ase` contains utility functions that can be used for interatomic calculations. It is inherited from merging https://github.com/openkim/kim-python-utils into this repo. Previously, it worked only with KIM Models. All new functions added to this module should support being passed both a KIM Model as a string, and an ASE `Calculator` object. See `kim-tools/ase/core.py::get_isolated_energy_per_atom` for an example. If you are working on or with existing functions in this module, try to upgrade it to this functionality.
distributed-memory-pool 0.1.1
A high-performance Python object sharing pool with distributed capabilities
Requires Python: >=3.7
Keywords: object pool, distributed, async, tcp, socket, serialization

# Memory Object Server

A high-performance Python object sharing pool with distributed capabilities.

## Features

- **Support for any Python object**: Including system-level objects like sockets
- **Asynchronous communication**: Built on asyncio for high performance
- **Minimal API**: Simple and intuitive interface
- **Distributed support**: Multi-node synchronization and discovery
- **Auto-reconnection**: Reliable TCP socket communication with automatic reconnection
- **Thread-safe**: Concurrent operations are fully supported
- **Port detection**: Smart startup with port availability checking

## Installation

```bash
pip install memory-obj-server
```

## Quick Start

### Server

```python
from memory_obj_pool import Server

server = Server(host='0.0.0.0', port=8765)
server.run()
```

### Client

```python
from memory_obj_pool import SyncClient

client = SyncClient(host='localhost', port=8765)

client.put('key', {'data': 'value'})
result = client.get('key')
print(result)  # {'data': 'value'}
```

### Async Client

```python
import asyncio
from memory_obj_pool import Client

async def main():
    client = Client(host='localhost', port=8765)
    await client.put('key', {'data': 'value'})
    result = await client.get('key')
    print(result)

asyncio.run(main())
```

## API Reference

### Server

- `Server(host='0.0.0.0', port=8765, max_size=10000)`: Create a server instance
- `server.run()`: Start the server (blocking)
- `await server.start()`: Start the server (async)
- `await server.stop()`: Stop the server

### Client

- `Client(host='localhost', port=8765, auto_reconnect=True)`: Create async client
- `await client.connect()`: Connect to server
- `await client.put(key, obj)`: Store an object
- `await client.get(key)`: Retrieve an object
- `await client.remove(key)`: Remove an object
- `await client.clear()`: Clear all objects
- `await client.size()`: Get pool size
- `await client.contains(key)`: Check if key exists

### SyncClient

- `SyncClient(host='localhost', port=8765, auto_reconnect=True)`: Create sync client
- `client.put(key, obj)`: Store an object
- `client.get(key)`: Retrieve an object
- `client.remove(key)`: Remove an object
- `client.clear()`: Clear all objects
- `client.size()`: Get pool size
- `client.contains(key)`: Check if key exists

## Distributed Usage

```python
from memory_obj_pool import DistributedPool

pool = DistributedPool(
    node_id='node1',
    known_nodes=[('localhost', 8765)]
)
pool.start()
```

## Requirements

- Python 3.7+
- dill
- asyncio

## License

Apache License 2.0
mdapy 1.0.1a1
Mdapy: molecular dynamics simulation analysis in Python
Requires Python: >=3.9

**mdapy** : Molecular Dynamics Analysis with Python
-----------------------------------------------------

Overview
---------

The **mdapy** provides a comprehensive suite of powerful, 
flexible, and straightforward tools for analyzing atomic 
trajectories generated from Molecular Dynamics (MD) simulations.

Resources
----------

- Homepage: https://github.com/mushroomfire/mdapy
- Documentation: https://mdapy.readthedocs.io/
- Issue Tracker: https://github.com/mushroomfire/mdapy/issues
- Tutorial: https://github.com/mushroomfire/mdapy-tutorial