semvec/pypi-packages huggingface dataset:
hugging-gan-test 0.2.29
Testing pip
Requires Python: >=3.6

# Geniverse library
The Geniverse Project. Created by Victor Perez (@viccpoes) & Javier Ideami (@ideami).
Welcome to Geniverse. Build and train a universe of state of the art generative models and use powerful tools to generate and create.

Geniverse Alpha is the first creative platform we are building. It is a tool that allows you to generate visual assets using generative A.I. models through a minimalistic and powerful prompt engineering interface. 

![Alt text](./media/repo/geniverse1.jpg?raw=true "Geniverse")

With Geniverse Alpha you can:
- Guide the generative A.I. architectures by creating prompts within a flexible canvas, using texts and images.
- Generate results using different cutting edge generate A.I. architectures, being ab le to tweak the key settings of those models.
- Create videos of interpolations between a set of results, and create videos of zoom processes beginning at one of the results.
- Download the images and videos generated by the A.I.
- Access the tool from any device. Geniverse Alpha is optimized for the Chrome browser on PC and Android, and the Safari browser on iOS Mac and iPhone.

Forks are integrated into this repo using [submodules](https://gist.github.com/gitaarik/8735255). To properly load the forks, the following command must be executed:
```python
git submodule init
```
![Alt text](./media/repo/geniverse2.jpg?raw=true "Geniverse")
playsh 0.0.2
A playground for OpenGL fragment shader

# playsh

A GLSL fragment shader playground, inspired by [Shadertoy](https://www.shadertoy.com/).





## Builtin shader uniforms

* uniform vec3 __iResolution__

* uniform float __iTime__

* iuniform float __TimeDelta__

* uniform int __iFrame__

* uniform vec4 __iMouse__

* uniform Texture2D __iChannel0__

* uniform Texture2D __iChannel1__

* uniform Texture2D __iChannel2__

* uniform Texture2D __iChannel3__

* uniform vec3 __iChannelResolution__[4]



## Dependencies

* glfw

* PyOpenGL

* PyGLM

* numpy

* pillow

* injector





## Example

```Python

from playsh import PlaySh, TextureDesc, Filter, Wrap



app = PlaySh(

    width=1920,

    height=1200,

    fragment_shader_path="examples/example.fs",

    channel0=TextureDesc(

        path="examples/noise.png", filter=Filter.LINEAR, wrap=Wrap.REPEAT

    ),

)

app.run()

```
cstream 1.0.4
C++ Style Colorful output stream for Python with debug level capabilities.
Requires Python: <4,>=3.7

# cstream
### C/C++ Style Colorful output stream for Python with level control and thread/process safety options.

<br>
<br>

## Installation
```bash
$ pip install cstream
```

## Introduction
Default `stderr`, `stdwar` and `stdlog` instances are directed to standard error output. Note that `stdout` is also available.

The `devnull` stram is intended to be used as a context manager in order to supress output directed to *stderr* and *stdout*.

Verbosity level control is coherent with the `logging` module and its constants are made available form importing.

## Examples
```python
from cstream import CStream, stderr, stdout, stdlog, stdwar, devnull, WARNING

# Set debug level
CStream.config(level=WARNING)

# Will be printed
stderr << "Error: You are 'redly' wrong"

# Gets printed also
stdwar << "Warning: Just a 'yellowish' warning..."

# Bypassed
stdlog << "DEBUG: Some blue text printed to stderr"

# Suppress all output written to stdout and stderr
with devnull:
    print("Bye World?")
```

## Threads and Multiprocessing
```python
from cstream import CStream

# For usage within threads
CStream.config(lock_type=CStream.T_LOCK)

# For multiprocessing
CStream.config(lock_type=CStream.P_LOCK)

# When no lock is needed (Default)
CStream.config(lock_type=None)
```

## Next steps:
- Complete `logging` integration.
- Thread-safe logging.
extracttoc 0.4.0
A CLI package to extract and create a table of contents from markdown files.
Requires Python: >=3.6

# extracttoc

[![PyPi](https://img.shields.io/pypi/v/extracttoc?color=blue&style=plastic)](https://pypi.org/project/extracttoc/)
![License](https://img.shields.io/github/license/Cribbersix/markdown-toc-extract?style=plastic)
[![CodeFactor](https://www.codefactor.io/repository/github/cribbersix/markdown-toc-extract/badge?style=plastic)](https://www.codefactor.io/repository/github/cribbersix/markdown-toc-extract)
![Repository size](https://img.shields.io/github/repo-size/Cribbersix/markdown-toc-extract?style=plastic)
[![made-with-python](https://img.shields.io/badge/Made%20with-Python-1f425f.svg?style=plastic)](https://www.python.org/)

Extract the table of contents from a markdown file with an easy to use command line tool written in Python.

## Installation

```sh
pip install extracttoc
```

## Usage


```sh
usage: extracttoc [-h] [-s] [-c] [-i] [-l LIMIT] file

Extracts the table of contents from a markdown file.

positional arguments:
  file                  Provide a markdown file from which to extract the toc.

optional arguments:
  -h, --help            show this help message and exit
  -s, --save            Write the TOC to a md file. File name will be: {input-file-name}-toc.md
  -c, --copy            Copy the TOC to your clipboard
  -i, --insert          Insert the TOC directly into the file in front of the first line.
  -l LIMIT, --levels LIMIT
                        Set the number of levels which will be included in the TOC.
```

## Examples

```python
> extracttoc myfile.md  # displays the TOC

> extracttoc -c myfile.md  # display & copy TOC to clipboard

> extracttoc -l 2 myfile.md  # limit TOC to header level 2

> extracttoc -l 2 -s myfile.md  # limit TOC to lvl 2 & write to separate file
```
solceasy 0.2
Um utilitário para compilação e deploy de contratos ethereum


Funciona assim: você salva o código na linguagem Solidity de um smart
contract(exemplo: MeuContrato) num arquivo MeuContrato.sol.

Esse utilitário irá pegar esse contrato, compilar, gerando o BIN e o ABI, e
irá gerar um arquivo MeuContrato.js pronto para ser executado, que irá lançar
o contrato a partir de um console geth.

Rode python3 pythonsolcbuild.py --help para informações.
classconvergence 1.2
Plot the class distribution as a function of iteration from a Class2D or Class3D job from RELION
Requires Python: >=3.9.7
Keywords: cryo-EM class distribution plot visualization

# classconvergence

[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.4732050.svg)](https://doi.org/10.5281/zenodo.4732050)

Plot the class distribution as a function of iteration from a Class2D or Class3D
job from RELION.

This tool was tested with star files produced by RELION-3.1.0. Earlier versions
of RELION are not supported.

## Acknowledgments

I would not have been able to put this tool together without the
[`starfile`](https://github.com/alisterburt/starfile) library.

## Installation

I recommend to install this tool in a dedicated conda environment. You can
create one like so (replace `ENV_NAME` with the name you want to give to this
environment):

```
$ conda deactivate
$ conda create --name ENV_NAME python=3.9
$ conda activate ENV_NAME
```

Once the conda environment is active, you can install the tool with the
following command:

```
$ pip install classconvergence
```

## Usage

```
$ classconvergence --help
Usage: classconvergence [OPTIONS] <job_directory>

  Plot the class distribution as a function of iteration from a Class2D or
  Class3D job from RELION.

Options:
  -c, --count        Plot particle counts per class (default, same effect as
                     not passing any option).

  -p, --percent      Plot percentages of particles per class (default:
                     counts).

  -o, --output TEXT  File name to save the plot (optional: with no file name,
                     simply display the plot on screen without saving it;
                     recommended file formats: .png, .pdf, .svg or any format
                     supported by matplotlib).

  -h, --help         Show this message and exit.
```