matplotlib

一、3D 图

在matploylib中创建3d图，首先要做的是创建 Axes3D 类

from mpl_toolkits.mplot3d.axes3d import Axes3D

1. 绘制曲面

import matplotlib.pyplot as pltfrom matplotlib import *from numpy import *from math import pifrom mpl_toolkits.mplot3d.axes3d import Axes3Dalpha = 0.7phi_ext = 2 * pi * 0.5def flux_qubit_potential(phi_m, phi_p):    return 2 + alpha - 2 * cos(phi_p)*cos(phi_m) - alpha * cos(phi_ext - 2*phi_p)phi_m = linspace(0, 2*pi, 100)phi_p = linspace(0, 2*pi, 100)X,Y = meshgrid(phi_p, phi_m)Z = flux_qubit_potential(X, Y).Tfig = plt.figure(figsize=(14,6))# `ax` is a 3D-aware axis instance because of the projection='3d' keyword argument to add_subplotax = fig.add_subplot(1, 2, 1, projection='3d')p = ax.plot_surface(X, Y, Z, rstride=4, cstride=4, linewidth=0)# surface_plot with color grading and color barax = fig.add_subplot(1, 2, 2, projection='3d')p = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.coolwarm, linewidth=0, antialiased=False)cb = fig.colorbar(p, shrink=0.5)fig

2. 绘制线框

fig = plt.figure(figsize=(8,6))ax = fig.add_subplot(1, 1, 1, projection='3d')p = ax.plot_wireframe(X, Y, Z, rstride=4, cstride=4)fig

3. 绘制投影轮廓

fig = plt.figure(figsize=(8,6))ax = fig.add_subplot(1,1,1, projection='3d')ax.plot_surface(X, Y, Z, rstride=4, cstride=4, alpha=0.25)cset = ax.contour(X, Y, Z, zdir='z', offset=-pi, cmap=cm.coolwarm)cset = ax.contour(X, Y, Z, zdir='x', offset=-pi, cmap=cm.coolwarm)cset = ax.contour(X, Y, Z, zdir='y', offset=3*pi, cmap=cm.coolwarm)ax.set_xlim3d(-pi, 2*pi);ax.set_ylim3d(0, 3*pi);ax.set_zlim3d(-pi, 2*pi);fig

4. 改变视图角度

view_init 可以改变视图角度，读取两个参数: elevation 与 azimuth 角度

fig = plt.figure(figsize=(12,6))ax = fig.add_subplot(1,2,1, projection='3d')ax.plot_surface(X, Y, Z, rstride=4, cstride=4, alpha=0.25)ax.view_init(30, 45)ax = fig.add_subplot(1,2,2, projection='3d')ax.plot_surface(X, Y, Z, rstride=4, cstride=4, alpha=0.25)ax.view_init(70, 30)fig.tight_layout()

二、动画

FuncAnimation 函数能根据一系列图生成动画，它有以下参数：

fig：图的画布

func：更新图的函数

init_func：初始化图的函数

frame：图的数量

blit：告诉动画函数只更新改动的部分:

def init():    # setup figuredef update(frame_counter):    # update figure for new frameanim = animation.FuncAnimation(fig, update, init_func=init, frames=200, blit=True)anim.save('animation.mp4', fps=30) # fps = frames per second

为了使用动画特性，首先加载模块 matplotlib.animation：

from matplotlib import animation# solve the ode problem of the double compound pendulum againfrom scipy.integrate import odeintg = 9.82; L = 0.5; m = 0.1def dx(x, t):    x1, x2, x3, x4 = x[0], x[1], x[2], x[3]    dx1 = 6.0/(m*L**2) * (2 * x3 - 3 * cos(x1-x2) * x4)/(16 - 9 * cos(x1-x2)**2)    dx2 = 6.0/(m*L**2) * (8 * x4 - 3 * cos(x1-x2) * x3)/(16 - 9 * cos(x1-x2)**2)    dx3 = -0.5 * m * L**2 * ( dx1 * dx2 * sin(x1-x2) + 3 * (g/L) * sin(x1))    dx4 = -0.5 * m * L**2 * (-dx1 * dx2 * sin(x1-x2) + (g/L) * sin(x2))    return [dx1, dx2, dx3, dx4]x0 = [pi/2, pi/2, 0, 0]  # initial statet = linspace(0, 10, 250) # time coordinatesx = odeint(dx, x0, t)    # solve the ODE problem

生成双摆的运动动画：

fig, ax = plt.subplots(figsize=(5,5))ax.set_ylim([-1.5, 0.5])ax.set_xlim([1, -1])pendulum1, = ax.plot([], [], color="red", lw=2)pendulum2, = ax.plot([], [], color="blue", lw=2)def init():    pendulum1.set_data([], [])    pendulum2.set_data([], [])def update(n):     # n = frame counter    # calculate the positions of the pendulums    x1 = + L * sin(x[n, 0])    y1 = - L * cos(x[n, 0])    x2 = x1 + L * sin(x[n, 1])    y2 = y1 - L * cos(x[n, 1])    # update the line data    pendulum1.set_data([0 ,x1], [0 ,y1])    pendulum2.set_data([x1,x2], [y1,y2])anim = animation.FuncAnimation(fig, update, init_func=init, frames=len(t), blit=True)# anim.save can be called in a few different ways, some which might or might not work# on different platforms and with different versions of matplotlib and video encoders#anim.save('animation.mp4', fps=20, extra_args=['-vcodec', 'libx264'], writer=animation.FFMpegWriter())#anim.save('animation.mp4', fps=20, extra_args=['-vcodec', 'libx264'])#anim.save('animation.mp4', fps=20, writer="ffmpeg", codec="libx264")anim.save('animation.mp4', fps=20, writer="avconv", codec="libx264")plt.close(fig)

为了生成动画，首先需要安装 libav-tools

sudo apt-get install libav-tools

运行：avplay animation.mp4

三、后端

Matplotlib 有很多用来渲染图片的后端：

print(matplotlib.rcsetup.all_backends)['GTK', 'GTKAgg', 'GTKCairo', 'MacOSX', 'Qt4Agg', 'TkAgg', 'WX', 'WXAgg', 'CocoaAgg', 'GTK3Cairo', 'GTK3Agg', 'WebAgg', 'agg', 'cairo', 'emf', 'gdk', 'pdf', 'pgf', 'ps', 'svg', 'template']

默认后端是 agg，适合生成光栅图，比如 PNG。

一般情况都不用切换后端，但如果希望生成高清矢量图，可以切换成 PDF 或者 GTKCairo。

1. 使用 svg 后端生成 svg 图片

# RESTART THE NOTEBOOK: the matplotlib backend can only be selected before pylab is imported!# (e.g. Kernel > Restart)import matplotlibmatplotlib.use('svg')import matplotlib.pylab as pltimport numpyfrom IPython.display import Image, SVG# Now we are using the svg backend to produce SVG vector graphicsfig, ax = plt.subplots()t = numpy.linspace(0, 10, 100)ax.plot(t, numpy.cos(t)*numpy.sin(t))plt.savefig("test.svg")# Show the produced SVG file. SVG(filename="test.svg")

2. 可交互后端

使用可交互后端时需调用 plt.show() 才能使图片显示出来。

# RESTART THE NOTEBOOK: the matplotlib backend can only be selected before pylab is imported!# (e.g. Kernel > Restart)import matplotlibmatplotlib.use('Qt4Agg') # or for example MacOSXimport matplotlib.pylab as pltimport numpy# Now, open an interactive plot window with the Qt4Agg backendfig, ax = plt.subplots()t = numpy.linspace(0, 10, 100)ax.plot(t, numpy.cos(t)*numpy.sin(t))plt.show()