semvec/pypi-packages huggingface dataset:
ds-example-plugin 1
Example plugin for data_slicer`s PIT.

# ds_example_plugin

A plugin for [`data_slicer`'s](https://github.com/kuadrat/data-slicer) PIT 
that simply serves as an example for how to make such a plugin.

It only adds minimal functionality to PIT, namely the function `blur` which
applies a Gaussian filter to the displayed data and the function 
`example_function` which just prints out a message to PIT's ipython console.  
As such, it really is not particularly useful beyond illustrative purposes.

Refer to the [documentation of `data_slicer`]() for more information.
pysimplewiki 0.1.2
Simple Wiki - это вики движок, отображающий файловую структуру в веб-энциклопедию; поддерживает статьи в форматах .html, .md. Simple Wiki написана на языке python, поддерживает гибкое расширение и стилизацию.
Requires Python: ~=3.8
Keywords: wiki, engine, markdown, web, server

**Simple Wiki** - это вики движок, отображающий файловую структуру в веб-энциклопедию; поддерживает статьи в форматах .html, .md. Simple Wiki написана на языке python, поддерживает гибкое расширение и стилизацию.
wikiserver 0.1.2
Simple Wiki - это вики движок, отображающий файловую структуру в веб-энциклопедию; поддерживает статьи в форматах .html, .md. Simple Wiki написана на языке python, поддерживает гибкое расширение и стилизацию.
Requires Python: ~=3.8
Keywords: wiki, engine, markdown, web, server

**Simple Wiki** - это вики движок, отображающий файловую структуру в веб-энциклопедию; поддерживает статьи в форматах .html, .md. Simple Wiki написана на языке python, поддерживает гибкое расширение и стилизацию.
kakaowork-py 0.1.0
Kakaowork Python client
Requires Python: >=3.6.1,<4.0.0
Keywords: kakaowork

# Kakaowork

[![CI](https://github.com/skyoo2003/kakaowork-py/actions/workflows/ci.yml/badge.svg?branch=master)](https://github.com/skyoo2003/kakaowork-py/actions/workflows/ci.yml) [![codecov](https://codecov.io/gh/skyoo2003/kakaowork-py/branch/master/graph/badge.svg?token=J6NQHDJEMZ)](https://codecov.io/gh/skyoo2003/kakaowork-py)

(Unofficial) Kakaowork Python client
restframework-serializer-permissions 0.0.2
permission based serializing for django restframework

restframework-serializer-permissions
====================================

Drop in replacement for [Django Restframework Serializers](https://www.django-rest-framework.org/api-guide/serializers/) to add permission based field serialization.

Installation
------------

Install this module into your environment:

    $ pip install restframework-serializer-permissions


Example
-------

This example uses a ModelSerializer as described in [DRF Docs](https://www.django-rest-framework.org/api-guide/serializers/#modelserializer)

```python
# import permissions from rest_framework
from rest_framework.permissions import AllowAny, IsAuthenticated

# import serializers from serializer_permissions instead of rest_framework
from serializer_permissions  import serializers

# import you models
from myproject.models import ShoppingItem, ShoppingList


class ShoppingItemSerializer(serializers.ModelSerializer):

    item_name = serializers.CharField()

    class Meta:
        # metaclass as described in drf docs
        model = ShoppingItem
        fields = ('item_name', )


class ShoppingListSerializer(serializers.ModelSerializer):

    # Allow all users to list name
    list_name = serializers.CharField(permission_classes=(AllowAny, ))

    # Only allow authenticated users to retrieve the comment
    list_comment = serializers.CharField(permissions=(IsAuthenticated, ))

    # show owner only, when the current user has 'auth.view_user' permission
    owner = serializers.CharField(permissions=('auth.view_user', ), hide=True)

    # serializer which is only available, when the user is authenticated
    items = ShoppingItemSerializer(many=True, permissions=(IsAuthenticated, ), hide=True)

    class Meta:
        # metaclass as described in drf docs
        model = ShoppingItem
        fields = ('list_name', 'list_comment', 'owner', 'items', )
```

You can also define your own permissions classes as described in the  [DRF documentation](https://www.django-rest-framework.org/api-guide/permissions/#custom-permissions) and specify multiple r`permission_classes` and r`permissions` on a field or serializer: all provided permissions must be satisfied for the visitor to retrieve the given field.


Compatibility
-------------

This package should be compatible with:

* Django Rest Framework 3
* Django 2.x, 3.x
* Python 3.6, 3.7, 3.8
vpscommacc-core 1.0.0
Набор полезных функций для повседневной работы и уменьшения размера кода
Requires Python: >=3.8

# Core VPScommacc

## file.py
* Отсортировать csv-файл по определенной колонке
* Записать одну строку в csv-файл
* Записать несколько строк в csv-файл

## mix.py
* Вычесть из первого списка элементы второго списка
* Найти элементы присутствующие в обоих списках

## path.py
* Получить все файлы из директории с полными путями (рекурсивно или нет)
* Получить путь к директории относительно текущей
* Получить путь к текущей директории


## regexp.py
* Очистить строку от всех html-тегов
* Получить все символы из текста (кириллица или латиница)
* Получить все цифры из текста


## time.py
* Получить порядковый номер дня недели
* Получить UNIX-метку
* Получить текущие дату и время
* Дата и время в русском формате
* Дата и время в английском формате
limobauwzq 2.0.5
sdfee
Requires Python: >=3.6, <4
Keywords: sample,setuptools,development

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pycolate 0.0.42
Generates site percolation data and illustrations.

# pycolate ☕

![Banner](https://raw.githubusercontent.com/Jackbytes/pycolate/main/images/cover_image.png)

A python implementation of site percolation, generates datasets and illustrations.

## Aims.

This project primarily generates datasets and illustrations from square lattice site percolation. There is an auxillary program which carries out critical probability estimation using coarse graining with possible interactions.

## Installation.

1. Install the latest version of pycolate:
```python
pip install pycolate
```
2.Import pycolate into your python file:
```python
import pycolate
```
## Creating illustrations.

The `pycolate.Percolation` class is used for creating a single instance of percolation. It takes the following arguments:
```python
Perc = Percolation(width, height, occupation probability)
```
We find the clusters by running:
 ```python
 Perc.cluster_find()
 ````
We display the illustration in a window as so:
```python
...
Perc.pretty_clusters() #OR Perc.simple_clusters() or Perc.only_percolating_cluster()
Perc.display()
```
To save the illsutration we run:
```python
...
Perc.save()
```
So to generate an illustration of a 50x50 grid where each square has a 0.6 chance of being occupied:
```python
import pycolate

perc = pycolate.Percolation(50,50,0.6)
perc.cluster_find()
perc.pretty_clusters()
perc.display() 
perc.save('PATH')
```
The default size for a square is 10 by 10 pixels. To adjust this one can change the `Percolation.site_size` prior to running any methods.

 If we were running a large simulation we may want the sites to only be 1 pixel each, so we would run the following:
```python
import pycolate

perc = pycolate.Percolation(1000,1000,0.6)

perc.site_site = 1
perc.cluster_find()
perc.pretty_clusters()
perc.display() 
perc.save('PATH')
```
To only display the percolating cluster in the illustration one can use `only_percolating_cluster('your_color')` instead of `pretty_clusters()` to create an illustration of the percolating cluster in the color 'your_color' (Must be a valid PIL color).

To display all the clusters in the same colour one can use `simple_clusters()` instead of `pretty_clusters()`. 

By default both of these methods use 'hotpink' as the default color if no color argument is given.

## Running experiments.
One may not be interested in illustrations and just want numeric data. We can compute many different properties by running the `Percolation.cluster_find()` method. Once this has been run there are many properties one can ask for:
### Properties of the Percolation class.
Property | Explanation |
--- | --- |
mean_cluster_size | The mean cluster size, excluding the percolating cluster. |
sizes | A list of all the cluster sizes, excluding the percolating cluster. |
percolated_size | The size of the percolating cluster. |
percolated | A boolean. True if a percolating cluster formed. False if not |