import matplotlib.pyplot as plt 
import numpy as np 

t = np.arange(0.0, 2.0, 0.01)#x轴上的点，0到2之间以0.01为间隔 
s = np.sin(2*np.pi*t)#y轴为正弦 
plt.plot(t, s)#画图 
 
plt.xlabel('time (s)')#x轴标签 
plt.ylabel('voltage (mV)')#y轴标签 
plt.title('About as simple as it gets, folks')#图的标签 
plt.grid(True)#产生网格 
plt.savefig("test.png")#保存图像 
plt.show()#显示图像 

x1 = np.linspace(0.0, 5.0)#画图一 
x2 = np.linspace(0.0, 2.0)#画图二 
y1 = np.cos(2 * np.pi * x1) * np.exp(-x1) 
y2 = np.cos(2 * np.pi * x2) 
 
plt.subplot(2, 1, 1)#面板设置成2行1列，并取第一个（顺时针编号） 
plt.plot(x1, y1, 'yo-')#画图，染色 
plt.title('A tale of 2 subplots') 
plt.ylabel('Damped oscillation') 
 
plt.subplot(2, 1, 2)#面板设置成2行1列，并取第二个（顺时针编号） 
plt.plot(x2, y2, 'r.-')#画图，染色 
plt.xlabel('time (s)') 
plt.ylabel('Undamped') 
 
plt.show() 

# -*- coding:utf-8 -*- 
import numpy as np 
import matplotlib.mlab as mlab 
import matplotlib.pyplot as plt 
 
mu = 100 # 正态分布的均值 
sigma = 15 # 标准差 
x = mu + sigma * np.random.randn(10000)#在均值周围产生符合正态分布的x值 
 
num_bins = 50 
n, bins, patches = plt.hist(x, num_bins, normed=1, facecolor='green', alpha=0.5) 
#直方图函数，x为x轴的值，normed=1表示为概率密度，即和为一，绿色方块，色深参数0.5.返回n个概率，直方块左边线的x值，及各个方块对象 
y = mlab.normpdf(bins, mu, sigma)#画一条逼近的曲线 
plt.plot(bins, y, 'r--') 
plt.xlabel('Smarts') 
plt.ylabel('Probability') 
plt.title(r'Histogram of IQ: $\mu=100$, $\sigma=15$')#中文标题 u'xxx' 
 
plt.subplots_adjust(left=0.15)#左边距 
plt.show() 

#!/usr/bin/env python 
# encoding: utf-8 
 
import matplotlib.pyplot as plt 
import numpy as np 
from mpl_toolkits.mplot3d import Axes3D 
 
x_list = [[3,3,2],[4,3,1],[1,2,3],[1,1,2],[2,1,2]] 
fig = plt.figure() 
ax = fig.gca(projection='3d') 
for x in x_list: 
  ax.scatter(x[0],x[1],x[2],c='r') 
plt.show()

from mpl_toolkits.mplot3d.axes3d import Axes3D 
from matplotlib import cm 
import matplotlib.pyplot as plt 
import numpy as np 
  
fig = plt.figure() 
ax = fig.add_subplot(1, 1, 1, projection='3d') 
X=np.arange(1,10,1) 
Y=np.arange(1,10,1) 
X, Y = np.meshgrid(X, Y) 
Z = 3*X+2*Y+30 
surf = ax.plot_surface(X, Y, Z, rstride=1, cstride=1,cmap=cm.jet,linewidth=0, antialiased=True) 
ax.set_zlim3d(0,100) 
fig.colorbar(surf, shrink=0.5, aspect=5) 
plt.show()  

from mpl_toolkits.mplot3d import Axes3D 
from matplotlib import cm 
from matplotlib.ticker import LinearLocator, FormatStrFormatter 
import matplotlib.pyplot as plt 
import numpy as np 
 
fig = plt.figure() 
ax = fig.gca(projection='3d') 
X = np.arange(-5, 5, 0.1) 
Y = np.arange(-5, 5, 0.1) 
X, Y = np.meshgrid(X, Y) 
R = np.sqrt(X**2 + Y**2) 
Z = np.sin(R) 
surf = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.coolwarm, linewidth=0, antialiased=False) 
#画表面,x,y,z坐标， 横向步长，纵向步长，颜色，线宽，是否渐变 
ax.set_zlim(-1.01, 1.01)#坐标系的下边界和上边界 
 
ax.zaxis.set_major_locator(LinearLocator(10))#设置Z轴标度 
ax.zaxis.set_major_formatter(FormatStrFormatter('%.02f'))#Z轴精度 
fig.colorbar(surf, shrink=0.5, aspect=5)#shrink颜色条伸缩比例（0-1），aspect颜色条宽度（反比例，数值越大宽度越窄） 
 
plt.show() 

# -*- coding: utf-8 -*- 
import matplotlib.pyplot as plt 
 
labels = 'Frogs', 'Hogs', 'Dogs', 'Logs'#设置标签 
sizes = [15, 30, 45, 10]#占比，和为100 
colors = ['yellowgreen', 'gold', 'lightskyblue', 'lightcoral']#颜色 
explode = (0, 0.1, 0, 0) #展开第二个扇形，即Hogs，间距为0.1 
 
plt.pie(sizes, explode=explode, labels=labels, colors=colors,autopct='%1.1f%%', shadow=True, startangle=90)#startangle控制饼状图的旋转方向 
plt.axis('equal')#保证饼状图是正圆，否则会有一点角度偏斜 
 
fig = plt.figure() 
ax = fig.gca() 
 
import numpy as np 
 
ax.pie(np.random.random(4), explode=explode, labels=labels, colors=colors,autopct='%1.1f%%', shadow=True, startangle=90, radius=0.25, center=(0, 0), frame=True) 
ax.pie(np.random.random(4), explode=explode, labels=labels, colors=colors, autopct='%1.1f%%', shadow=True, startangle=90, radius=0.25, center=(1, 1), frame=True) 
ax.pie(np.random.random(4), explode=explode, labels=labels, colors=colors, autopct='%1.1f%%', shadow=True, startangle=90, radius=0.25, center=(0, 1), frame=True) 
ax.pie(np.random.random(4), explode=explode, labels=labels, colors=colors, autopct='%1.1f%%', shadow=True, startangle=90,radius=0.25, center=(1, 0), frame=True) 
 
ax.set_xticks([0, 1])#设置位置 
ax.set_yticks([0, 1]) 
ax.set_xticklabels(["Sunny", "Cloudy"])#设置标签 
ax.set_yticklabels(["Dry", "Rainy"]) 
ax.set_xlim((-0.5, 1.5)) 
ax.set_ylim((-0.5, 1.5)) 
 
ax.set_aspect('equal') 
plt.show()