semvec/pypi-packages huggingface dataset:
Topsis-Gurmandeep-102303764 0.1.1
Implementation of TOPSIS (Technique for Order Preference by Similarity to Ideal Solution)
Requires Python: >=3.7

# Topsis-Gurman-102303764

This package implements the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method in Python.

## Installation

pip install Topsis-Gurmandeep-102303764

## Usage

Run the package from the command line:

python -m topsis_gurmandeep_102303764.topsis InputDataFile Weights Impacts OutputFile

### Example

python -m topsis_gurmandeep_102303764.topsis data.csv "1,1,1,2,1" "+,+,+,+,+" output.csv

## Input Requirements

- Input file must be a CSV file
- First column should contain alternatives
- Remaining columns should contain numeric criteria
- Weights must be numeric and comma separated
- Impacts must be + or - and comma separated

## Output

The output file will contain:
- TOPSIS Score
- Rank

## Dependencies

- numpy
- pandas

## Author

Gurmandeep Kaur
memory-obj-server 0.2.0
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
libcasm-global 2.4.0
Constants, definitions, and generically useful tools used by CASM
Requires Python: <3.15,>=3.10

<img alt="Shows the CASM logo" src="https://raw.githubusercontent.com/prisms-center/CASMcode_global/main/python/doc/_static/logo.svg" width="600" />

#### libcasm-global

The libcasm-global package provides generically useful tools used by CASM:

- CASM global constants and definitions
- Input / output tools, especially for JSON parsing and formatting
- An Eigen distribution and methods using Eigen
- Helpers for runtime library compiling and linking
- Miscellaneous mathematical functions
- Other tools for C++ development


#### Install

    pip install libcasm-global


#### Usage

See the [libcasm docs](https://prisms-center.github.io/CASMcode_pydocs/libcasm/overview/latest/).


#### About CASM

The libcasm-global package is part of the [CASM](https://prisms-center.github.io/CASMcode_docs/) open source software package, which is designed to perform first-principles statistical mechanical studies of multi-component crystalline solids.

CASM is developed by the Van der Ven group, originally at the University of Michigan and currently at the University of California Santa Barbara.

For more information, see the [CASM homepage](https://prisms-center.github.io/CASMcode_docs/).


#### License

GNU Lesser General Public License (LGPL). Please see the file LICENSE for details.