semvec/pypi-packages huggingface dataset:
pyness 1.3.7
Tool for quick necessary stuff
Requires Python: >=3.5

## Badges
[![Downloads](https://static.pepy.tech/personalized-badge/pyness?period=total&units=international_system&left_color=black&right_color=orange&left_text=Downloads)](https://pepy.tech/project/pyness?versions=1.0.0&versions=1.2.0&versions=1.1.9&versions=1.1.8&versions=1.1.7&versions=1.1.6&versions=1.1.5&versions=1.1.4&versions=1.1.1&versions=1.0.9&versions=1.0.5&versions=1.0.6&versions=1.0.8&versions=1.0.7&versions=1.1.0&versions=1.1.3&versions=1.1.2&versions=1.3.0)

# what is Pyness
pyness --> python necessary.

A simple module aka tool that will cover some necessary work of your project/work.
The codes on this module is very simple i think anyone can do this but this module will help you to do it quickly and make your lines low.

# important
If u got any error pls make issus in github

# Update log
- Sorry for those who got error on version - 1.3.6 (i forgot to add pillow module on requirement)
- key function now using half secrets and half random module
- convert.photo is removed

## Next Update:
- Working on bites
- i will use bites to make the key function even more faster
- new function (key.number,key.upper) #
- new function when done working on bites (bitestostr,bitestoint,str/int to bites)
## Install

```bash
  pip install pyness
```


## Deployment

Generate with key function:
```python
x1 = pyness.key(5)
x2 = pyness.key(5,number=True,upper=False,lower=False,symbol=False)
x3 = pyness.key(5,number=3,upper=2)
x4 = pyness.key(5,number="1",upper="SA",lower=False,symbol=False)
```
output:
```python
x1 : &qYkJ    All mixed
x2 : 98003    only number
x3 : I70Q9    there is 3 number and 2 upper
x4 : A11S1    used string
```
Get FPS:
```python
def test(x,b):
    x + b
    print(pyness.fps())
```
Bar:
```python
a = pyness.bar(total=100,amount=20,length=50)
print(a)
```
output:
```python
"|██████████----------------------------------------| 20%"
```
update:
```python
pyness.update()
```
output:
```python
Collecting pyness
  Using cached pyness-1.2.0-py3-none-any.whl (3.8 kB)
Installing collected packages: pyness
Successfully installed pyness-1.2.0
```
## Features
- key
- FPS
- Bar
- coming soon
pie-torch 0.0.1
N-dimensional Poisson image editing implemented with Pytorch and NumPy
Requires Python: >=3.8

# PIE-torch: Poisson Image Editing in Pytorch

Fast, n-dimensional Poisson image editing.

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/matt-baugh/pytorch-poisson-image-editing/blob/master/examples.ipynb)

2 implementations, including:
 - using Green Function Convolution, as described in [Fast and Optimal Laplacian Solver for Gradient-Domain Image Editing using Green Function Convolution](https://arxiv.org/abs/1902.00176)
 - using a Discrete Sine Transform, following [OpenCV's implementation](https://github.com/opencv/opencv/blob/3f4ffe7844cead4e406bfb0067e9dae2ff9247f3/modules/photo/src/seamless_cloning_impl.cpp#L323)

Recommendations:
 - For blending images with consistent boundaries, use `blend`, the Green Function Convolution implementation. 
 - For images with inconsistent boundaries, use `blend_dst_numpy`.

Main interface:
 - `blend`: primary entrypoint, blends source image into target image at specified coordinates.
 - `blend_dst_numpy`: entrypoint for DST-based blending (currently only available in NumPy).
 - `CachedPoissonBlender`: calls `blend` but caches the Green function, so should be faster if you're repeatedly
blending source patches of equal size, as you will only need to construct the Green function once.
 - `blend_numpy`: A NumPy implementation of `blend`.
   - `blend_wide[_numpy]`: Wrappers of `blend` methods which allow for blending over entire image to more smoothly integrate the source region.
   
Why use it?
 - It's faster than any available alternative (OpenCV's `seamlessClone`, or manual solvers using iterative methods).
 - It's flexible, working on n-dimensional images, with no explicit limitations on data types
   (unlike `seamlessClone`, which only operates on 8-bit 3-channel images).
 - You using it makes me feel like I'm contributing to the world.

## Installation

### Using pip

... coming soon ...

### Manually
Clone PIE-torch repository and install the package locally:

```bash
git clone https://github.com/matt-baugh/pytorch-poisson-image-editing.git
pip install -e ./pytorch-poisson-image-editing
```

You can check the installation by running the tests:

```bash
cd pytorch-poisson-image-editing/test
pytest
```

## Usage

Example of blending normal images using mixed gradients:
```python
from pietorch import blend

target : torch.Tensor = ... # 3 x N x M image to be blended into
source : torch.Tensor = ... # 3 x H x W image to be blended
mask : torch.Tensor = ... # H x W mask of which pixels from source to be included
corner : torch.Tensor = ... # [y, x] coordinate of location in target for source to be blended

result = blend(target, source, mask, corner, True, channels_dim=0)
```

A wide variety of full examples are given in [the examples notebook](https://github.com/matt-baugh/pytorch-poisson-image-editing/blob/master/examples.ipynb).
validatorcli 0.1.0
Valicator CLI

# Welcome to the Validator Command Line Tools.

I validate code

## What tools are included?

1. [validator] - a cli which provides linting validation of Infrasfructure as
   Code.

## How to install?

### Requirements

To install the validatorcli we have a few requirements.

* [Python 3.8]

[Python 3.8]: https://www.python.org/downloads/release/python-386/
[validator]: ./docs/validator.md
hue-api 0.4.8
Async API and CLI tools for interacting with Hue Lights
Requires Python: ~=3.8
Keywords: hue, lights, async, automation

# Hue API

> _Async API and CLI tools for interacting with Hue Lights_

[![Python Tests](https://github.com/nirantak/hue-api/actions/workflows/python-test.yml/badge.svg)](https://github.com/nirantak/hue-api/actions/workflows/python-test.yml)
[![Publish Package](https://github.com/nirantak/hue-api/actions/workflows/python-publish.yml/badge.svg)](https://github.com/nirantak/hue-api/actions/workflows/python-publish.yml)
![Python Versions](https://img.shields.io/pypi/pyversions/hue-api)
[![Package Version](https://img.shields.io/pypi/v/hue-api)](https://pypi.org/project/hue-api/)

Documentation: [hue-api.nirantak.com](https://hue-api.nirantak.com/)

Source: [github.com/nirantak/hue-api](https://github.com/nirantak/hue-api)

## Installation

This is an async client to interact with the Hue Bridge API.

The minimum Python version required to run this is 3.8

Install the package using pip:

```bash
pip install hue-api
```

## Usage

Follow [this link](https://developers.meethue.com/develop/get-started-2/#so-lets-get-started) to create a Hue API user if not already known ([API reference](https://developers.meethue.com/develop/hue-api/7-configuration-api/#create-user))

### Using the CLI

```bash
hue --help

# Get the version of hue-api installed
hue version

# To find your Hue Bridge IP address go to discovery.meethue.com, or run:
hue bridge discover
```

### Using the API

```python
from hue import Bridge, Light

# Create a light object with the light id (number), Bridge IP and user
light = Light(1, ip="your-hue-bridge-ip", user="hue-api-user")

# from an async function
async def main():
  await Bridge.discover()
  await light.power_on()

# or from a sync context
import asyncio
asyncio.run(Bridge.discover())
asyncio.run(light.power_on())
```

## Changelog

See the file [CHANGELOG.md](CHANGELOG.md)

## License

This project is licensed under the terms of the [MIT license](https://github.com/nirantak/hue-api/blob/main/LICENSE)