semvec/pypi-packages huggingface dataset:
paginator.py 0.9.4
Simple to use discord paginator for messages and embeds with reactions and buttons.
License: MIT
Requires Python: >=3.6
Keywords: discord paginator discord-paginator

<div>
  <h1 align='center'>
    paginator.py
  </h1>
</div>
<div>
  <p align='center'>
    <img src=https://img.shields.io/pypi/dm/paginator.py?color=success&label=PyPi%20Downloads&style=flat-square>
    <img src=https://shields.io/github/issues-raw/FlamptX/discord-paginator?color=success&label=Active%20Issues&style=flat-square>
    <img src=https://img.shields.io/badge/Latest_Version-0.9.4-informational>
  </p>
  <p align='center'>
    Simple to use discord paginator for messages and embeds with reactions and buttons.
  </p>
</div>
<br>

## Features
- Very easy to use
- Customisable
- Change pages with buttons or reactions
- Actively maintained
## Links
- **[Documentation](https://flampt.gitbook.io/paginator)**
- **[PyPi](https://pypi.org/project/paginator.py)**
## Installation
You can easily install it using the python package manager `pip`

```
pip install paginator.py
```

## Quickstart
Here are some examples that might help.
### Sending a message with pages that uses reaction

```python
from paginator import Paginator, Page, NavigationType
from discord import Embed

...

paginator = Paginator(bot)


@bot.command()
async def test(ctx):
    pages = [
        Page(content="React!", embed=Embed(title="Page #1", description="Testing")),
        Page(embed=Embed(title="Page #2", description="Still testing")),
        Page(embed=Embed(title="Page #3", description="Guess... testing"))
    ]

    await paginator.send(ctx.channel, pages, type=NavigationType.Reactions)
```
### Sending a message with pages that uses buttons

```python
from paginator import Paginator, Page, NavigationType
from discord import Embed

...

paginator = Paginator(bot)


@bot.command()
async def test(ctx):
    pages = [
        Page(content="Click!", embed=Embed(title="Page #1", description="Testing")),
        Page(embed=Embed(title="Page #2", description="Still testing")),
        Page(embed=Embed(title="Page #3", description="Guess... testing"))
    ]

    await paginator.send(ctx.channel, pages, type=NavigationType.Buttons)
```
## Changing the emojis

```python
from paginator import Paginator, PageEmojis

...

paginator = Paginator(bot)

class Emojis(PageEmojis):
    def __init__(self):
        super().__init__()

        self.back = "⏪"
        self.forward = "⏩"

paginator.page_emojis = Emojis()
```
## Contributions
Feel free to open pull requests and improve the library. If you find any issues, please report it.
EdgeClassifier 0.0.11
A python package for classify edges of graph based on topological features and neural networks.
License: MIT
Requires Python: >=3.6.8
Keywords: gpu,graph,edges,edge,classification,neural,networks

# Edge Prediction
Graph's edges classification by topological (and other) features with neural network.

## Installation
* Installation via pip:
```
pip install
```
* Installation with git:
```
git clone https://github.com/louzounlab/Edge-Prediction.git
```

## How to use?
```python
import networkx as nx
from EdgeClassifier.edge_classifier import EdgeClassifier

# Build networkx graph from edges list:
graph = nx.read_edgelist("./data/graph1.txt", delimiter=",", create_using=nx.DiGraph,
                         data=(("label", int), ("attribute1", float,)))
graph = nx.convert_node_labels_to_integers(graph)

# Build the classifier.
classifier = EdgeClassifier("./pkl", "./plots", verbose=True, gpu=False)

# Define parameters to the graph and to the model and execute.
classifier.build("graph5", graph, {
    "lr": 0.001,
    "batch_size": 64,
    "epochs": 150
}, topological_features=None, data_features=["attribute1"])

```



This package classify graphs' edges by , graph edges classification by topological attributes

> Attention! This package uses non boost graph-measures, and that's might make the features calculation slower. 
> If you would like to clac them in boost environment, follow the instruction here (link), and move th .pkl file to the pkl directory.