semvec/pypi-packages huggingface dataset:
django-simplemde 0.1.4
django-simplemde is a WYSIWYG markdown editor for Django
License: MIT
Keywords: django,admin,wysiwyg,markdown,editor,simplemde

# A markdown editor(with preview) for Django
Use markdown editor https://github.com/sparksuite/simplemde-markdown-editor in django project, this project is inspired by https://github.com/douglasmiranda/django-wysiwyg-redactor/ 

# Getting started
* install django-simplemde
```
pip install django-simplemde
```

* add 'simplemde' to INSTALLED_APPS.

```python
INSTALLED_APPS = (
    # ...
    'simplemde',
    # ...
)
```

# Using in models
```python
from django.db import models
from simplemde.fields import SimpleMDEField

class Entry(models.Model):
    title = models.CharField(max_length=250, verbose_name=u'Title')
    content = SimpleMDEField(verbose_name=u'mardown content')
```

# SimpleMDE options
You could set SimpleMDE options in settings.py like this:

```python
SIMPLEMDE_OPTIONS = {
    'placeholder': 'haha',
    'status': False,
    'autosave': {
        'enabled': True
    }
}
```

Right now this plugin supports [SimpleMDE Configurations](https://github.com/sparksuite/simplemde-markdown-editor#configuration), but only the static ones(don't support js configurations like ```previewRender```)

***for autosave option, you dont need to set it, this plugin will generate uniqueId with python's uuid.uuid4 automatically***

# Get SimpleMDE instance from DOM

After SimpleMDE initialized, you could get SimpleMDE instance from dom element like this:

```javascript
$('.simplemde-box')[0].SimpleMDE
```
outgraph 1.0.0
Outlier detection algorithm for graph datasets
License: MIT
Requires Python: >=3
Keywords: outlier-detection,outlier,graph,mahalanobis

# outgraph

`outgraph` is a simple outlier detection algorithm for graph datasets. Given a list of graphs, it uses [Mahalanobis distance](https://en.wikipedia.org/wiki/Mahalanobis_distance) detect which graphs are outliers based on either their topology or node attributes.

> Note: `outgraph` only works for datasets where each graph has an equal number of nodes.

## Installation

You can install `outgraph` with `pip`:

```bash
$ pip install outgraph
```

## How it Works

Unlike most approaches to graph outlier detection, `outgraph` does not use machine learning. Instead, each graph is converted into a vector representation using one of three available methods:

1. Averaging the node feature/attribute vectors
2. Flattening the adjacency matrix
3. A concatenation of 1 and 2

Then, the [Mahalanobis distance](https://en.wikipedia.org/wiki/Mahalanobis_distance) between each vector and the distribution of vectors is calculated. Lastly, a [Chi-Squared distribution](https://en.wikipedia.org/wiki/Chi-squared_distribution) is used to model the distribution of distances and identify the distances outside a cutoff threshold (e.g. p < 0.05).

This approach is based off [this article.](https://towardsdatascience.com/multivariate-outlier-detection-in-python-e946cfc843b3)

## Usage

Each graph in your dataset needs to be an instance of `outgraph.Graph`. This object has two parameters, `node_attrs` and `adjacency_matrix` –– both numpy arrays where the indices correspond to nodes. Example:

```python
import numpy as np
from outgraph import Graph

node_attrs = np.array([[-1], [0], [1]])
adj_matrix = np.array([[1, 1, 0],
                       [1, 1, 1],
                       [0, 1, 1]])
graph = Graph(node_attrs, adj_matrix)
```

<p align="center">
  <img src="example_graph.svg" width="35%" />
  <br />
</p>

Once you have a list of `Graph` objects, simply submit them into `outgraph.detect_outliers`:

```python
from outgraph import Graph, detect_outliers

graphs = [Graph(), ...]
outliers, indices = detect_outliers(graphs, method=1, p_value=0.05)
```

Notice the `method` and `p_value` parameters. The `method` parameter is an integer between 1 and 3 that corresponds to one of the three graph vectorization methods described in the ![How it Works](##How it Works) section. `p_value` is the outlier cutoff threshold.
neko.py 1.0.1
An API Wrapper for the NekoBot API
License: MIT
Keywords: neko,nekobot,neko.py,nekobot.py,neko.py

# Neko.py

A Python API Wrapper for [NekoBot API](https://docs.nekobot.xyz/#image-generation-threats)

# usage

```py
import neko

client = neko.NekoClient(token)

async def get_image():
    image = await client.generate_image('baguette', 'https://example.com/image.jpg')
    return image.url


async def main():
    image_url = await get_image()
    print(image_url)

asyncio.run(main())
```
flake8-pytest-fixtures-style 0.0.3
A flake8 extension that checks pytest fixtures
License: MIT
Requires Python: >=3.8
Keywords: flake8

# flake8-pytest-fixtures-style

[![Build Status](https://travis-ci.com/sidorov-as/flake8-pytest-fixtures-style.svg?branch=main)](https://travis-ci.org/sidorov-as/flake8-pytest-fixtures-style)
[![Maintainability](https://api.codeclimate.com/v1/badges/705e9a1c834a48e1d05c/maintainability)](https://codeclimate.com/github/sidorov-as/flake8-pytest-fixtures-style/maintainability)
[![Test Coverage](https://api.codeclimate.com/v1/badges/705e9a1c834a48e1d05c/test_coverage)](https://codeclimate.com/github/sidorov-as/flake8-pytest-fixtures-style/test_coverage)
[![PyPI version](https://badge.fury.io/py/flake8-pytest-fixtures-style.svg?)](https://badge.fury.io/py/flake8-pytest-fixtures-style)
![PyPI - Python Version](https://img.shields.io/pypi/pyversions/flake8-pytest-fixtures-style)

A flake8 extension that checks pytest fixtures.

## Installation

```terminal
pip install flake8-pytest-fixtures-style
```

## Usage

```terminal
$ flake8 text.py
text.py:2:5: PF001: pytest fixture "your_func_name" returning another fixture must be suffixed with "_factory"
```

## Error codes

| Error code |                     Description   |
|:----------:|:---------------------------------:|
|   [PF001](docs/codes/PF001.md)   | fixture `factories` must be suffixed with "_factory" |
|   [PF002](docs/codes/PF002.md)   | fixtures unused in test function body must be specified via a `@pytest.mark.usefixtures(...)` |