摘要：
S=np.cos（x），alpha=0.5）#图。绘图（x，alpha=0.25）###注释#t=1#绘图。plot（[t，xy=（t，数据分析常用图##散点图#fig=plt.figure（）#ax=fig.add_subplot（3，n）#随机数#t=np.arctan2（Y，
#encoding=utf-8
import numpy as np
def main():
import matplotlib.pyplot as plt
##lesson1:画图
# x = np.linspace(-1, 1, 50)
# x=np.linspace(-np.pi,np.pi,256,endpoint=True)
# c,s=np.cos(x),np.sin(x)
# plt.figure(1)
# plt.plot(x,c,color="blue",linewidth=1.0,linestyle="-",label="COS",alpha=0.5)
# plt.plot(x,s,"r*",label="SIN")
# plt.title("COS & SIN")
# ##设置坐标轴
# ax=plt.gca()
# ax.spines["right"].set_color("none")
# ax.spines["top"].set_color("none")
# ax.spines["left"].set_position(("data",0))
# ax.spines["bottom"].set_position(("data",0))
# ax.xaxis.set_ticks_position("bottom")
# ax.yaxis.set_ticks_position("left")
# ##网格填充
# plt.grid()
# plt.fill_between(x,np.abs(x)<0.5,c,c>0.5,color="green",alpha=0.25)
# ##注释
# t=1
# plt.plot([t,t],[0,np.cos(t)],"y",linewidth=3,linestyle="--")
# plt.annotate("cos(1)",xy=(t,np.cos(1)),xycoords="data",xytext=(+10,+30),textcoords="offset points",arrowprops=dict(arrowstyle="->",connectionstyle="arc3,rad=.2"))
#
# plt.show()
##lesson2:数据分析常见图形
##散点图scatter
# fig=plt.figure()
# ax=fig.add_subplot(3,3,1)
# n=128
# X=np.random.normal(0,1,n)#随机数
# Y=np.random.normal(0,1,n)#随机数
# T=np.arctan2(Y,X)#上色
# #plt.axes([0.025,0.025,0.95,0.95])#指定现实范围
# #plt.scatter(X,Y,s=75,c=T,alpha=0.5)#画散点
# ax.scatter(X, Y, s=75, c=T, alpha=0.5) # 画散点
# plt.xlim(-1.5,1.5),plt.xticks([])#X范围
# plt.ylim(-1.5,1.5),plt.yticks([])#Y范围
# plt.axis()
# plt.title("scatter")
# plt.xlabel("x")
# plt.ylabel("y")
# plt.show()
# #bar柱状图
# fig = plt.figure()
# fig.add_subplot(332)
# n=10
# X=np.arange(n)
# Y1=(1-X/float(n))*np.random.uniform(0.5,1.0,n)
# Y2=(1-X/float(n))*np.random.uniform(0.5,1.0,n)
#
# plt.bar(X,+Y1,facecolor='#9999ff',edgecolor='white')
# plt.bar(X,-Y2,facecolor='#ff9999',edgecolor='white')
# for x,y in zip(X,Y1):
# plt.text(x+0.4,y+0.05,'%.2f'%y,ha='center',va='bottom')
#
# for x,y in zip(X,Y2):
# plt.text(x+0.4,-y-0.05,'%.2f'%y,ha='center',va='top')
#
# plt.show()
# #pie饼状图
# fig = plt.figure()
# fig.add_subplot(333)
# n=20
# Z=np.ones(n)
# Z[-1]*=2
# plt.pie(Z,explode=Z*.05,colors=['%f'%(i/float(n)) for i in range(n)],
# labels=['%.2f'%(i/float(n)) for i in range(n)])
# plt.gca().set_aspect('equal')
# plt.xticks([]),plt.yticks([])
# plt.show()
# #polar极线图
# fig = plt.figure()
# fig.add_subplot(334,polar=True)
# n=20
# theta=np.arange(0.0,2*np.pi,2*np.pi/n)
# radii=10*np.random.rand(n)
# #plt.plot(theta,radii)
# plt.polar(theta,radii)
# plt.show()
#heatmap热力图
# fig=plt.figure()
# fig.add_subplot(335)
# from matplotlib import cm
# data=np.random.rand(3,3)
# cmap=cm.Blues
# map=plt.imshow(data,interpolation='nearest',cmap=cmap,aspect='auto',vmin=0,vmax=1)
# plt.show()
# #3D图
# fig=plt.figure()
# ax=fig.add_subplot(336,projection="3d")
# ax.scatter(1,1,3,s=100)
# plt.show()
# #hot map热图
# fig=plt.figure()
# fig.add_subplot(313)
# def f(x,y):
# return (1-x/2+x**5+y**3)*np.exp(-x**2-y**2)
# n=256
# x=np.linspace(-3,3,n)
# y=np.linspace(-3,3,n)
# X,Y=np.meshgrid(x,y)
# plt.contourf(X,Y,f(X,Y),8,alpha=.75,cmap=plt.cm.hot)
# plt.show()
#
# ##保存图片
# plt.savefig("./data/fig.png")
if __name__ == '__main__':
main()