MatplotlibMatplotlib架构概述
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 class:
FigureCanvas: matplotlib.backened_bas.FigureCnvas
Encompass the area onto which the figure is drawn
例如画纸
Renderer: matplotlib.backened_bas.Renderer
Knows how to draw on the FigureCanvas
例如画笔
Event: matplotlib.backend_bas.Event
Handles ur inputs such as keyboard strokes and mou clicks
Artist Layer (Artist)
Comprid of one main object - Artist:
Knows how to u 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 U
# 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.t_title('Normal distribution with $\mu=0, \sigma=1$')
fig.savefig('matplotlib_histogram.png')
Scripting Layer (pyplot)
⽇常⽤途，更简洁
Comprid mainly of pyplot, a scripting interface that is lighter that the Artist layer.
Let’s e 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()
