semvec/pypi-packages huggingface dataset:
kadota 1.1.0
Tools for working with Figma programmatically
License: MIT

# Kadota

Tools for working with Figma programmatically. 

Package page can be found here: https://pypi.org/project/kadota/.

# Working with this repository

For creating and regenerating environment file:
```
conda env create --file env.yaml
conda env export --no-builds | grep -v "^prefix: " > env.yaml
```

For creating requirements file required for pip installability (may need to install pipreqs):
```
pipreqs . --force
```

For deleting old builds:
```
rm build dist -rf
```

For building before publishing to pypi:
```
python setup.py sdist bdist_wheel
```

For testing before publishing to pypi:
```
twine check dist/*
```

For publishing to pypi:
```
twine upload --repository-url https://upload.pypi.org/legacy/ dist/*
```
## Demo
```
import kadota.utils
import kadota.figmatools

if __name__ == "__main__":
    """
    Function demonstrating usage of kadota.
    """

    # Load keys from keys.json and create a FigmaFile object to access a specific file.
    keys = kadota.utils.load_keys()
    figma_file = kadota.figmatools.FigmaFile(
        keys["figma-access-token"], 
        keys["figma-file-id"]
    )

    # Retrive all the elements.
    print(f"Found {len(figma_file.get_elements())} elements in file.")

    # Retrieve a page id.
    page_name = "templates"
    page_id = figma_file.get_page_ids_by_name(page_name)[0]
    print(f"Found page '{page_name}' with id {page_id}.")

    # Retrieve all the elements with a specific name.
    element_name = "linkedin-square"
    print(f"Found {len(figma_file.get_elements(element_name=element_name))} element(s) with name {element_name}.")
```
talkkeeper 0.2.2
Библиотека Python talkkeeper для загрузки в хранилище / предобработки / разметки / ...
License: MIT
Requires Python: >=3.10,<4.0

# Обработчик Media файлов

Задача: Обработать звук, разложить его на объекты и положить в систему так, чтобы над данными можно было выполнять операции

## Интерфейс Talkkeeper
-[] Получение meta информации
-[x] Вычисление хеша
-[] Базовая аналитика над объектом
-[] Хранение метрик клиента
-[] Разбиение объекта на блоки
-[] Создание карты объекта
-[] Поддержка интерфейса talkkeeper server
-[] Поддержка чтения 
-[x] Обход файлов системы
-[] Загрузка индекса файлов в бд
  -[] Схема данных в базе
  -[] Создание таблиц и базы при первом запуске
liteshort 1.3.0
User-friendly, actually lightweight, and configurable URL shortener
License: MIT
Requires Python: >=3.8

# liteshort
liteshort is a link shortener designed with lightweightness, user and sysadmin-friendliness, privacy, and configurability in mind.

Click [here](https://ls.ikl.sh) for a live demo

(Unfortunately, I had to restrict creation of shortlinks due to malicious use by bad actors, but you can still see the interface.)

*Why liteshort over other URL shorteners?*

liteshort is designed with the main goal of being lightweight. It does away with all the frills of other link shorteners and allows the best of the basics at a small resource price. liteshort uses under 20 MB of memory idle, per worker. liteshort has an easy-to-use API and web interface. liteshort doesn't store any more information than necessary: just the long and short URLs. It does not log the date of creation, the remote IP, or any other information.

liteshort focuses on being configurable. There are over 15 config options and most updates will add more. Configuration is done through the easy-to-use YAML format.


![liteshort screenshot](https://fs.ikl.sh/selif/1qu5x7sj.png)

## Installation

Liteshort's installation process is dead-simple. Check out the [installation page](https://git.ikl.sh/132ikl/liteshort/wiki/How-to-Install) for info on how to install.
imgaug3 0.4.1
Image augmentation library for deep neural networks
License: MIT
Keywords: augmentation,image,deep learning,neural network,CNN,machine learning,computer vision,overfitting

A library for image augmentation in machine learning experiments, particularly convolutional
neural networks. Supports the augmentation of images, keypoints/landmarks, bounding boxes, heatmaps and segmentation
maps in a variety of different ways.