semvec/pypi-packages huggingface dataset:
directdb 1.2.3
An async package that makes database handling extremely easy!
Keywords: db, bot, discord bot, database, postgresql, asyncpg, async, pgutils, nosql, sqlite, aiosqlite, discord.py



# directdb

A simple library that makes handling SQL databases in python easy without the need to understand the syntax. The library would act like an interface between your code and the database server parsing the data to SQL format.

If you enjoy using this project, consider giving it a star as it helps out a ton <3

Currently Supported Databases:
- PostgreSQL
- SQLite

## Github Repository

https://github.com/cannonballchris/directdb

## Installation

To install the library, use `pip install directdb`

    
## License

[GPL-3.0](https://choosealicense.com/licenses/gpl-3.0/)


## Documentation

[Documentation](https://indigodev.gitbook.io/directdb)


## Usage/Examples

Using the library with a discord bot

```py
import asyncio

import discord
from discord.ext import commands
from directdb import Postgresql

class MyBot(commands.Bot):
    def __init__(self):
        super().__init__(command_prefix = "!", intents = discord.Intents.all())
    
    async def setup_hook(self):
        setattr(self, "db", Postgresql(
            host = "localhost",
            user = "username",
            password = "password",
            database = "Database name here",
            port = 5000 #Your db port address here
        ))
        await self.db.connect()
        print("DB Ready")

if __name__ == "__main__":
    asyncio.run(MyBot().run("TOKEN"))
```

Using the library normally without a discord bot context.

```py
import asyncio

from directdb import Postgresql

async def database(host, user, password, database, port):
    db = Postgresql(host = host, user= user, password = password, database = database, port = port)
    await db.connect()

asyncio.run(database(...))
```





## Contributing

You can contribute to this project by providing valueable suggestions and reporting issues in our [Discord Server](https://discord.gg/sj2c7gzPzE)
spanns 1.1.1
The Survival Probability Adapted Nuclear Norm Shrinkage Denoiser
Requires Python: >=3.8

The Survival Probability Adapted Nuclear Norm Shrinkage (SPANNS) Denoiser
=========================================================================

A simple denoiser compatible with VapourSynth.

Supported Formats
-----------------
The denoiser core supports any 2D numeric matrices. For VapourSynth usage, you need to provide clips with full precision and planes of the same dimensions (e.g., convert YUV422 to YUV444PS or RGBS first).

Usage
-----

`spanns.spanns(clip, ...)`

### Arguments:
- **clip** (*vs.VideoNode*): The original video node.
- **sigma** (*int*): Denoising strength. Default is `1`.
- **tol** (*float*): Noise tolerance in the range [0, 1]. Default is `0.7`.
- **gamma** (*float*): Texture threshold in the range [0, 1]. Higher values preserve less texture. Default is `0.5`.
- **passes** (*int*): Number of denoising steps. Default is `2`.
- **ref1** (*vs.VideoNode*): Reference clip, an approximation of the result. Default is a median filter.
- **ref2** (*vs.VideoNode*): Reference clip, a blurred one obtained from the original. It serves as an alternative way to control denoising strength. If provided, `sigma` will be ignored. Default is a box blur with radius equal to `sigma`.
- **planes** (*Sequence[int]*): Indices of planes to process. Default is `[0, 1, 2]`.

### Returns:
The denoised clip (*vs.VideoNode*).

TODO
----
- Implement GPU acceleration.

Background
----------
In 2019, the author was disappointed by the practical performance of WNNM, a well-regarded but (f\*\*\*\*\*\*)cumbersome denoising approach proposed years earlier. Although there have been improvements to the technique, most rely on patch matching and increasing complexity, which the author finds unappealing. Reflecting on its limitations, the author implemented a better (but still sh\*t) heuristic method: SPANNS.

In essence, SPANNS damps the orthogonal components according to the noise's survival probability along the axis of component magnitude (the singular values). While the denoising results might not stand out in today's advanced landscape, the author hopes someone can appreciate its simplicity.

Buy Me Coffee
-------------
Don't, I can't have coffee.
Topsis-Harshil-102217020 1.0.0
A Python package to implement the TOPSIS algorithm for decision-making.
Requires Python: >=3.6

# Topsis-Harshil-102217020

A Python package to implement the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) algorithm for multi-criteria decision-making.

## Features
- Handles multi-criteria decision-making problems.
- Supports weighted and impact-based criteria (+/-).
- Outputs TOPSIS scores and ranks.

## Installation

Install the package using pip:

```bash
pip install Topsis-Harshil-102217020