semvec/pypi-packages huggingface dataset:
matplotlib-dashboard 0.0.4
This package, with the aim of accelerating and facilitating the use of some design patterns, offers abstract classes.
Requires Python: >=3.6

# Matplotlib Dashboard Package

This package creates a simple user interface for creating subfigures when working with matplotlib.

<div style="overflow-y: scroll; max-height: 250px">

```python
import matplotlib.pyplot as plt
plt.figure(figsize=(10,10))

from matplotlib_dashboard import MatplotlibDashboard
dashboard = MatplotlibDashboard([
    ['top' ,'top' ,'top' ,'top'  ],
    ['left','left', None ,'right'],
    ['left','left','down','right'],
], as3D=['left'], wspace=0.5, hspace=0.5)

import numpy as np
dashboard['top'].plot(np.random.rand(200), color='red')
dashboard['top'].set_ylabel('y')
dashboard['top'].set_xlabel('x')
dashboard['top'].set_title('top plot')

dashboard['right'].bar(['A','B','C'], [10,35,17], color='blue')
dashboard['right'].set_ylabel('freq')
dashboard['right'].set_xlabel('label')
dashboard['right'].set_title('right bar')

from PIL import Image
dashboard['down'].imshow(Image.open('./test1.jpeg'))
dashboard['down'].get_xaxis().set_ticks([])
dashboard['down'].get_yaxis().set_ticks([])
dashboard['down'].set_title('down image')

z = ((5-np.arange(100)%10)**3).reshape(10,10)
x, y = np.meshgrid(np.arange(z.shape[0]), np.arange(z.shape[1]))
dashboard['left'].plot_surface(x, y, z, color='green')
dashboard['left'].set_ylabel('x')
dashboard['left'].set_xlabel('y')
dashboard['left'].set_zlabel('z')
dashboard['left'].set_title('left surface')

plt.show()
```

</div>
<br/>

<img src="https://github.com/BehzadShayegh/matplotlib-dashboard/blob/master/examples/example1/output.png?raw=true" style="width: 75%;" />

## Customization

* Pass the map of subfigures you want when getting an instance of the dashboard. Write the map as a list of lists or a 2D array.
* Get the subfigure you want using get-item syntax. The name is what you defined in the map.
* Use the `None` keyword to access the empty part of the map.
* Use `as3D` argument to determine which subfigures are 3D.
* Any customization on `matplotlib.pyplot` also applies to this dashboard. For example, use `plt.figure(figsize=(x,y))` to change the size of the dashboard figure.
* This module is based on `matplotlib.gridspec`. Therefore, you can use the `gridspec` initialization configurable parameters with the same functionality in this dashboard initialization. For example, set the `wspace` and `hspace` parameters to change the horizontal or vertical spacing between subfigures.

## Installation
```pip install matplotlib-dashboard```