Matplotlib | 架构概述

Less is more effective
Less is more attractive
Less is more impactive

Matplotlib architecture

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Matplotlib体系结构分为三层，可以将其视为堆栈。位于另一层之上的每一层都知道如何与它下面的层进行通信，但是下层却不知道它上面的层。从下到上的三层是：Backend, Artist, Scripting Layer.

Backend Layer (FigureCanvas, Renderer, Event)

Has three built-in abstract interface classes:

• FigureCanvas: matplotlib.backened_bases.FigureCnvas
  • Encompasses the area onto which the figure is drawn
  • 例如画纸
• Renderer: matplotlib.backened_bases.Renderer
  • Knows how to draw on the FigureCanvas
    • 例如画笔
• Event: matplotlib.backend_bases.Event
  • Handles user inputs such as keyboard strokes and mouse clicks

Artist Layer (Artist)

• Comprised of one main object - Artist:
  • Knows how to use the Renderer to draw on the canvas.
  • 在matplotlib中看到的所有内容Figure都是一个 Artist实例。
• Title, lines, tick labels, and images, all correspond to individual Artist instances.
• Two types of Artist objects:
  • Primitive: Line2D, Rectangle, Circle, and Text
  • Composite: Axis, Tick, Axes, and Figure
• Each artist may contain other composite artists as well as primitive artists.

一个用Artist作图例子~

# Putting the Artist Layer to Use
# generate a histogram of some data using the Artist layer
from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas # import FigureCanvas
from matplotlib.figure import Figure # import Figure artist
fig = Figure()
canvas = FigureCanvas(fig)

# create 10000 random numbers using numpy
import numpy as np
x = np.random.randn(10000)

ax = fig.add_subplot(111) # create an axes artist

ax.hist(x, 100) # generate a histgram of the 10000 numbers

# add a little to the figure and save it
ax.set_title('Normal distribution with $\mu=0, \sigma=1$')
fig.savefig('matplotlib_histogram.png')

Scripting Layer (pyplot)

• 日常用途，更简洁
• Comprised mainly of pyplot, a scripting interface that is lighter that the Artist layer.
• Let’s see how we can generate the same histogram of 10000 random values using the pyplot interface

import matplotlib.pyplot as plt
import numpy as np

x = np.random.randn(10000)

plt.hist(x, 100)
plt.title(r'Normal distribution with $\mu=0, \sigma=1$')
plt.savefig('matplotlib_histogram.png')
plt.show()