semvec/pypi-packages huggingface dataset:
amino-rus 1.2
Асинхронная русскоязычная библиотека для взаимодействия с API Amino
Keywords: aminoapps,amino,amino-rus,amino_rus,amino-bot,amino_bot,amino-py,amino_py,amino.py,narvii,api

Асинхронная библиотека для взаимодействия с https://aminoapps.com/api и https://service.narvii.com/api/v1, собранная в одном файле. Все функции имеют русскоязычные имена.
bavard 0.0.57
A library and CLI for NLP and chatbot tasks
Requires Python: >=3.6

# bavard-nlu

## Local Development

### Install Dependencies

```
pip3 install -e .
pip3 install -r requirements-test.txt
```

### Testing Locally

The tests for this repo consist of functional tests for catching bugs/code regressions, validation tests for catching model performance regressions, and integration tests for testing the NLU docker containers and their interactions. The validation and integration tests take much longer to execute. All are run in CI. To run the tests locally:

```
./test/scripts/lint-and-test.sh <test_suite>
```

Where `<test_suite` denotes the type of tests to run, and allows these options:
- `functional` - Runs just the functional tests.
- `integration` (default) - Runs both the functional and integration tests.
- `all` - Runs the functional, integration, and validation tests.

To run both the functional and integration tests:

### The Package CLI

There is a convenience CLI for training, evaluating, predicting, tuning, and interacting with NLU and dialogue policy models. To see the CLI documentation:

```
python3 -m bavard --help
```

You can also view the documentation for a sub-command for example:

```
python3 -m bavard nlu evaluate  --help
```

## Releasing The Package

Releasing the package is automatically handled by CI, but three steps must be taken to trigger a successful release:

1. Increment the `VERSION` variable in `setup.py` to the new desired version (e.g. `VERSION="1.1.1"`)
2. Commit and tag the repo with the **exact same** value you populated the `VERSION` variable with (e.g. `git tag 1.1.1`)
3. Push the commit and tag to remote. These can be done together using: `git push --atomic origin <branch name> <tag>`

CI will then release the package to pypi with that version once the commit and tag are pushed.
voxelobj 0.0.1
simple voxel library
Requires Python: >=3.6
Keywords: python,voxel,obj,3D

# voxel_obj_lib_in_python
This is a simple libary for creating voxel 3D-obj files in python. There is another implementation in cpp (link: https://github.com/martinpflaum/voxel_obj_lib_in_cpp)

```WorldGrid``` takes as first parameter the 3d grid size of the world. In our example the grid would be of size 4x4x4. The second parameter of ```WorldGrid``` is the size of the colorpallete. With a colorpallete of size 8, the textures you want to apply onto the object should be in a 8x8 grid. A 8x8 grid is for example 64x64 - each texture is then 8x8 pixel large. Its not that complicated just try some textures with same width and hight out.

In ```__setitem__``` the first argument are the x,y,z coordinate of the cube you want to change. The second argument of ```__setitem__``` is the colorvalue of the voxel. -1 stands for void. The colorvalue 0 corresponds to the pixel value at point 0,0 in texture palette. The obj() function returns the string that contains the objfile
```python
from voxelobj import *
worldGrid = WorldGrid(4,8)
worldGrid[1,1,1] = 1
worldGrid[0,1,1] = -1
worldGrid[1,0,1] = -1
worldGrid[1,1,0] = -1
worldGrid[2,1,1] = -1
worldGrid[1,2,1] = -1
worldGrid[1,1,2] = -1
write("output.obj",worldGrid.obj())
```
4chan-biz-mentions 0.3.0
Count the times a word is mentionned, can be used to track a shitcoin

# 4chan-biz-mentions

HF tracking your shitcoin, loser

## Install

```sh
pip install 4chan-biz-mentions
```

## Usage

```sh
> python3 -m 4chan_biz_mentions moon eth avax ada sol btc ankr algo fud bullish scam pump nft
sol mentionned 44 times
eth mentionned 39 times
pump mentionned 22 times
scam mentionned 19 times
moon mentionned 12 times
fud mentionned 8 times
ada mentionned 7 times
avax mentionned 6 times
btc mentionned 5 times
algo mentionned 3 times
bullish mentionned 3 times
nft mentionned 3 times
ankr mentionned 0 times
```

[shithub](http://github.com/arthuRHD)
ATUS-Transformer 1.0.1
A package that transforms activity variables to fewer categories
Requires Python: >=3.6

# ATUS Transformer package

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

This package contains two functions. One of them transforms the original ATUS activity codes into 11 broad categories: 
1. Sleep
2. Personal Care
3. Housework
4. Child Care
5. Adult Care
6. Work and Education
7. Shopping
8. TV Watching
9. Eating
10. Leisure
11. Travel and Other

The second function recodes transformed activity codes into string names.

## Installation

```
pip install ATUS-Transformer
```

## Usage

- There is (for now) 2 functions:
  - activityDictionary(). The arguments is a string containing ATUS original activity code.
  - activityNumberToString(). The argument is a number between 1 and 11.

## Example: activityDictionary

```
import atus_transformer
atus_transformer.activityDictionary("10101")
```
**Output**

```
1
```

## Note

- Contributions are welcome. Contact: kamilakolpashnikova@gmail.com

## Author

Kamila Kolpashnikova 2021
easylatex2image 0.0.1
another latex converter 2 pictures
Requires Python: >=3.6
Keywords: python,latex,image,png,jpg

# Another Latex Converter 2 Image
This is a simple converter from latex to pillow images. The main function is latex_to_image. The input packages_and_commands must include all packages and custom enviroments, custom commands. Just write it as normal latex. In packages_and_commands do not use \begin{document} just simple usepacke stuff and commands! The content argument is the content that should be visualized. This literally can be annything. We are using a real latexcompiler in the backend. out_file_name is a string or None if you don't want a file to bewritten. dpi is the resolution. Higher is better but with more storage. 


The output type is a pillow image so you can convert it to a numpy array with np.array(pillow_image) and then go from there. Also ofcourse you can safe it as png with pillow_image.save().

``` python
import easylatex2image
from easylatex2image import latex_to_image

packages_and_commands = r"""\usepackage[parfill]{parskip}
\usepackage[german]{varioref}
\usepackage{url}
\usepackage{amsmath} 
\usepackage{dcolumn}
\usepackage{tikz}
\usetikzlibrary{shapes,arrows}
\usetikzlibrary{intersections}
\usepackage[all,cmtip]{xy}
"""

content = r"""
\xymatrix{M \ar[d]_\kappa \ar[r]^f & A\\ K \ar[ur]_{f_K}}
"""
pillow_image = latex_to_image(packages_and_commands,content,"output.jpg",dpi=500,img_type="JPEG")
```

Also with the r before the string you can just pass the latex code as it is without \n or something like that.
# Installation 
The libary needs miktex (link: https://miktex.org) to be installed. If you have AVast installed miktex might not work. A possible solution would to
1. go to menu
2. go to settings.
3. go to execptions.
4. add pdflatex command as execption.
Avast also might ask you if you want to make an execptions if you reboot. 

Alternative Solution: Use Linux :)