semvec/pypi-packages huggingface dataset:
BubbleNova 0.0.5
A Python library for running multiple Discord bots at once.
Keywords: python,discord,disord bot,bubble nova

# BubbleNova

BubbleNova is a Python framework developed to simplify the concurrent execution of multiple Discord bots (or other scripts). It achieves this by running the bots in separate threads, allowing them to operate independently and concurrently.

## How it Works

BubbleNova operates based on a dictionary input that contains the name of your bot (or script) and its directory path relative to the script where you import BubbleNova. It starts each bot in its own thread, ensuring they run in separate, isolated environments.

## Usage

To use BubbleNova, follow these steps:

1. Import the `BubbleNova` library into your Python script.

```python
import BubbleNova
```

2. Create a dictionary that maps your bot names to their respective script directories:

```python
bot_list = {
    "SoapBot": "bots/SoapBot.py",
    "Bubbles": "bots/Bubbles.py"
}
```

3. Start the bots using the start_bots function, passing in your bot list as an argument:

```python
BubbleNova.start_bots(bot_list)
```

When you run your script, BubbleNova will launch each bot in a separate thread, allowing them to operate concurrently.

# Example

Here's a sample code snippet demonstrating how to use BubbleNova:

```python
import BubbleNova

bot_list = {
    "SoapBot": "bots/SoapBot.py",
    "Bubbles": "bots/Bubbles.py"
}

BubbleNova.start_bots(bot_list)

print("This code here can still run even though the bots are also still online!")
```
cornerplots 1.0
Simple library to create corner plots in `matplotlib`.
Requires Python: >=3.11

# Cornerplots

Simple library to create corner plots in `matplotlib`. Corner plots provide a simple way to visualize multidimensional data, with each dimension plotted against every other dimension. The main goal is to let the user specify data and/or model predictions in arbitrary subsets of the multi-dimensional space and let the code take care of where to plot these values. Originally inspired by [corner.py](https://github.com/dfm/corner.py).

## Simple example

You might have one set of (*w*, *x*, *y*) data, another set of (*x*, *y*, *z*) data, and a model predicting (*w*, *x*, *y*, *z*) trajectories.

```py
import numpy as np

t = np.linspace(0, 1)
model = dict(
    w = 3 * t + 1,
    x = 5 * t,
    y = 7 * t**.5,
    z = 3 * t**1.5,
    )

red_data = dict(
    w = np.array([1, 2, 3, 4]),
    x = np.array([0, 2, 4, 5]),
    y = np.array([1, 4, 6, 8]),
    )

blue_data = dict(
    x = np.array([1, 2, 5]),
    y = np.array([3, 5, 7]),
    z = np.array([0, 1, 3]),
    )
```

In that case, you could plot everything by simply calling:



```py
from cornerplots import Cornerplots
import matplotlib.pyplot as ppl

fig = ppl.figure(figsize = (5,5))

cp = Cornerplots(
    fields = ['w', 'x', 'y', 'z'],
    labels = ['W-value', 'X-value', 'Y-value', 'Z-value'],
    grid_kwargs = {'alpha': 0.25, 'lw': 0.7},
    )

cp.plot(model,     'k-', lw = 1, dashes = (6,2,2,2), label = 'model')
cp.plot(red_data,  'ws', mec = 'r', mew = 1, ms = 5, label = 'red data')
cp.plot(blue_data, 'wD', mec = 'b', mew = 1, ms = 5, label = 'blue data')

cp.legend()

ppl.savefig('example.png')
```


<div align="center">
<img src="example.png">
</div>