matplotlib画图

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3. colormap图

<p >

'+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')

4. 饼状图

import matplotlib.pyplot as plt
quants: GDPlabels: 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 Plota@R_502_6622@ct: format of "percent" string;
plt.pIE(quants,explode=expl,colors=colors,labels=labels,a@R_502_6622@ct='%1.1f%%',pctdistance=0.8,shadow=True)
plt.Title('top 10 GDP CountrIEs',bBox={'facecolor':'0.8','pad':5})
plt.show()