概述

参考文档：

nofollow">matplotlib画饼状图

1. 图中加标注

plt
import numpy as np
x=np.linspace(-1,1,10)
y=x**2  
fig=plt.figure(figsize=(8,4))
ax=plt.subplot(111)
plt.plot(x,y)
for i,(_x,_y) in enumerate(zip(x,y)):
    plt.text(_x,_y,i,color='red',fontsize=i+10)
plt.text(0.5,0.8,'subplot words',color='blue',ha='center',transform=ax.trans    Axes)
plt.figtext(0.1,0.92,'figure words',color='green')
plt.annotate('buttom',xy=(0,0),xytext=(0.2,0.2),arrowprops=dict(facecolor='blue',shrink=0.05))
plt.show()

<p style="text-align:center;">

plt
import numpy as np
dict = {'A': 40,'B': 70,'C': 30,'D': 85}
for i,key in enumerate(dict):#Circulate both index and value(Here is key)
    plt.bar(i,dict[key],color='r',width=0.2)
plt.xticks(np.arange(len(dict))+0.1,dict.keys())#Translation
plt.yticks(dict.values())
plt.grid(True)
plt.show()

3. colormap图

<p style="text-align:center;">

'+fn)
            #fn='/d3/MWRT/R20130805/F06925_EMS60.txt'
            data=wlab.dlmread(fn)
            EMS=EMS+list(data[:,1])#地表发射率
            LST=LST+list(data[:,2])#温度
            TBH=TBH+list(data[:,8])#水平亮温
            TBV=TBV+list(data[:,9])#垂直亮温
    #-----------------------------------------------------------
    #生成格点数据，利用griddata插值
    grid_x,grid_y = np.mgrid[275:315:1,0.60:0.95:0.01]
    grid_z = griddata((LST,EMS),TBH,(grid_x,grid_y),method='cubic')
    #将横纵坐标都映射到（0，1）的范围内
    extent=(0,1)
     #指定colormap
    cmap = matplotlib.cm.jet
    #设定每个图的colormap和colorbar所表示范围是一样的，即归一化
    norm = matplotlib.colors.Normalize(vmin=160,vmax=300)
    #显示图形，此处没有使用contourf #>>>ctf=plt.contourf(grid_x,grid_y,grid_z)
    gci=plt.imshow(grid_z.T,extent=extent,origin='lower',cmap=cmap,norm=norm)
    #配置一下坐标刻度等
    ax=plt.gca()
    ax.set_xticks(np.linspace(0,9))
    ax.set_xticklabels( ('275','280','285','290','295','300','305','310','315'))
    ax.set_yticks(np.linspace(0,8))
    ax.set_yticklabels( ('0.60','0.65','0.70','0.75','0.80','0.85','0.90','0.95'))
    #显示colorbar
    cbar = plt.colorbar(gci)
    cbar.set_label('$T_B(K)$',fontdict=font)
    cbar.set_ticks(np.linspace(160,300,8))
    cbar.set_ticklabels( ('160','180','200','220','240','260','300'))
    #设置label
    ax.set_ylabel('Land Surface Emissivity',fontdict=font)
    ax.set_xlabel('Land Surface Temperature(K)',fontdict=font) #陆地地表温度LST
    #设置title
    titleStr='$T_B$ for Freq = '+str(float(fp[1:-1])*0.01)+'GHz'
    plt.title(titleStr)
    figname=fp+'.png'
    plt.savefig(figname)
    plt.clf()#清除图形
plt.show()
print('ALL -> Finished OK')

print('ALL -> Finished OK')

print('ALL -> Finished OK')

4. 饼状图

40376
Germany     3099080
Russia      2383402
Brazil      2293954
UK          2260803
France      2217900
Italy       1846950

import matplotlib.pyplot as plt

quants: GDP
labels: country name
labels   = []

quants   = []
Read data
for line in file('../data/major_country_gdp'):

info = line.split()

labels.append(info[0])

quants.append(float(info[1]))
make a square figure
plt.figure(1,figsize=(6,6))
For China,make the piece explode a bit
def explode(label,target='China'):

if label == target: return 0.1

else: return 0

expl = map(explode,labels)
Colors used. Recycle if not enough.
colors  = ["pink","coral","yellow","orange"]
Pie Plot
autopct: format of "percent" string;
plt.pie(quants,explode=expl,colors=colors,labels=labels,autopct='%1.1f%%',pctdistance=0.8,shadow=True)

plt.title('Top 10 GDP Countries',bBox={'facecolor':'0.8','pad':5})
plt.show()

labels = []
quants = []

for line in file('../data/major_country_gdp'):
info = line.split()
labels.append(info[0])
quants.append(float(info[1]))

plt.figure(1,figsize=(6,6))

def explode(label,target='China'):
if label == target: return 0.1
else: return 0
expl = map(explode,labels)

colors = ["pink","coral","yellow","orange"]

plt.pie(quants,explode=expl,colors=colors,labels=labels,autopct='%1.1f%%',pctdistance=0.8,shadow=True)
plt.title('Top 10 GDP Countries',bBox={'facecolor':'0.8','pad':5})

plt.show()

labels = []
quants = []

for line in file('../data/major_country_gdp'):
info = line.split()
labels.append(info[0])
quants.append(float(info[1]))

plt.figure(1,figsize=(6,6))

def explode(label,target='China'):
if label == target: return 0.1
else: return 0
expl = map(explode,labels)

colors = ["pink","coral","yellow","orange"]

plt.pie(quants,explode=expl,colors=colors,labels=labels,autopct='%1.1f%%',pctdistance=0.8,shadow=True)
plt.title('Top 10 GDP Countries',bBox={'facecolor':'0.8','pad':5})

plt.show()

总结

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