import matplotlib.pyplot as plt 
import numpy as np 

t = np.arange(0.0, 2.0, 0.01)#x軸上的點，0到2之間以0.01為間隔 
s = np.sin(2*np.pi*t)#y軸為正弦 
plt.plot(t, s)#畫圖 
 
plt.xlabel('time (s)')#x軸標簽 
plt.ylabel('voltage (mV)')#y軸標簽 
plt.title('About as simple as it gets, folks')#圖的標簽 
plt.grid(True)#產(chǎn)生網(wǎng)格 
plt.savefig("test.png")#保存圖像 
plt.show()#顯示圖像 

x1 = np.linspace(0.0, 5.0)#畫圖一 
x2 = np.linspace(0.0, 2.0)#畫圖二 
y1 = np.cos(2 * np.pi * x1) * np.exp(-x1) 
y2 = np.cos(2 * np.pi * x2) 
 
plt.subplot(2, 1, 1)#面板設置成2行1列，并取第一個（順時針編號） 
plt.plot(x1, y1, 'yo-')#畫圖，染色 
plt.title('A tale of 2 subplots') 
plt.ylabel('Damped oscillation') 
 
plt.subplot(2, 1, 2)#面板設置成2行1列，并取第二個（順時針編號） 
plt.plot(x2, y2, 'r.-')#畫圖，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 # 正態(tài)分布的均值 
sigma = 15 # 標準差 
x = mu + sigma * np.random.randn(10000)#在均值周圍產(chǎn)生符合正態(tài)分布的x值 
 
num_bins = 50 
n, bins, patches = plt.hist(x, num_bins, normed=1, facecolor='green', alpha=0.5) 
#直方圖函數(shù)，x為x軸的值，normed=1表示為概率密度，即和為一，綠色方塊，色深參數(shù)0.5.返回n個概率，直方塊左邊線的x值，及各個方塊對象 
y = mlab.normpdf(bins, mu, sigma)#畫一條逼近的曲線 
plt.plot(bins, y, 'r--') 
plt.xlabel('Smarts') 
plt.ylabel('Probability') 
plt.title(r'Histogram of IQ: $\mu=100$, $\sigma=15$')#中文標題 u'xxx' 
 
plt.subplots_adjust(left=0.15)#左邊距 
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) 
#畫表面,x,y,z坐標， 橫向步長，縱向步長，顏色，線寬，是否漸變 
ax.set_zlim(-1.01, 1.01)#坐標系的下邊界和上邊界 
 
ax.zaxis.set_major_locator(LinearLocator(10))#設置Z軸標度 
ax.zaxis.set_major_formatter(FormatStrFormatter('%.02f'))#Z軸精度 
fig.colorbar(surf, shrink=0.5, aspect=5)#shrink顏色條伸縮比例（0-1），aspect顏色條寬度（反比例，數(shù)值越大寬度越窄） 
 
plt.show() 

# -*- coding: utf-8 -*- 
import matplotlib.pyplot as plt 
 
labels = 'Frogs', 'Hogs', 'Dogs', 'Logs'#設置標簽 
sizes = [15, 30, 45, 10]#占比，和為100 
colors = ['yellowgreen', 'gold', 'lightskyblue', 'lightcoral']#顏色 
explode = (0, 0.1, 0, 0) #展開第二個扇形，即Hogs，間距為0.1 
 
plt.pie(sizes, explode=explode, labels=labels, colors=colors,autopct='%1.1f%%', shadow=True, startangle=90)#startangle控制餅狀圖的旋轉方向 
plt.axis('equal')#保證餅狀圖是正圓，否則會有一點角度偏斜 
 
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])#設置位置 
ax.set_yticks([0, 1]) 
ax.set_xticklabels(["Sunny", "Cloudy"])#設置標簽 
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()