Less is more effective
Less is more attractive
Less is more impactive
这里记下关于Matplotlib体系结构的笔记,主要是上课内容加上自己找的资料,夹英夹中,大家可以当个参考。
Matplotlib架构概述
Matplotlib architecture
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()