def draw_3data(data1,data2,data3,min,max,my_color=1,label=''): import matplotlib.pyplot as plt X,Y=np.meshgrid(np.arange(data1.shape[1]),np.arange(data1.shape[0])) interval_of_cf=subroutine.cl_res(min,max) fig=plt.figure() fig=plt.figure(figsize=(8,15)) plt = Cmap[my_color].Set_Cmap(plt) ax1=fig.add_subplot(3,1,1) ax1.contourf(X, Y,data1,interval_of_cf, extend = 'both') ax2=fig.add_subplot(3,1,2) ax2.contourf(X, Y,data2,interval_of_cf, extend = 'both') ax3=fig.add_subplot(3,1,3) CF=ax3.contourf(X, Y,data3,interval_of_cf, extend = 'both') _,_,cb_interval,_=subroutine.color(data3,my_color,interval_of_cf,ax3) cax = fig.add_axes([0.95, 0.15, 0.04, 0.7]) #[左端、下端、幅、高さ] cb=plt.colorbar(CF,cax,ticks=cb_interval) cb.ax.tick_params(labelsize=15) cb.set_label(label,fontsize=20) return plt
def minus_MG_and_argopoint(year,month,var,cb_min,cb_max,area_num,depth): # area_num=0でインド洋全域 import subroutine import MOAA_GPV import WOA01 import AQC import numpy as np import matplotlib.pyplot as plt stryear,strmonth=subroutine.strym(year,month) strym=stryear+strmonth save_dir=subroutine.save_dir() my_color=0 # 青白赤 dataWOA=WOA01.nc_read(month,var,depth+1) # WOAはz=0があるので、1足してMGと一致 dataWOA=subroutine.data_trimming_IO(dataWOA,4) dataMG=MOAA_GPV.nc_read(year,month,var,depth) dataMG=subroutine.data_trimming_IO(dataMG,1) data=dataMG-dataWOA xgrid,ygrid=subroutine.IO_gridvalue() m=subroutine.IO_map(area_num,1,1) x, y = np.meshgrid(xgrid, ygrid) X, Y = m(x, y) title='' plt.title(title,fontsize=25) temp,salt,pres,lon,lat=AQC.get_data(year,month) interval_of_cf=subroutine.cl_res(cb_min,cb_max) data,cmap,cb_interval,plt=subroutine.color(data,my_color,interval_of_cf,plt) CF=m.contourf(X, Y, data,interval_of_cf, cmap=cmap, latlon=True,extend='both') cb=m.colorbar(CF, ticks=cb_interval) SC=plt.scatter(lon,lat,s=45,c='maroon') fnameF = save_dir+strym+var+'_MG-WOA.jpg' plt.savefig(fnameF) plt.clf()
def ColorContour(plt, X, Y, data, parameter, label_flg = 0): cb_min = parameter[0] cb_max = parameter[1] div = parameter[2] clabel = parameter[3] interval_of_cf=subroutine.cl_res(cb_min,cb_max, div = div) CF=plt.contourf(X, Y,data,interval_of_cf, extend = 'both') if label_flg == 0: plt.colorbar(CF, label = clabel) return plt, CF
# x軸、y軸の値のデータを読み込む
dt=np.dtype([("xgrid","<51f")])
fd=open("/home/yu/Dropbox/workspace/data_assimilation/Optimal_interpolation/xgrid.out","r")
chunk=np.fromfile(fd,dtype=dt,count=1)
xgrid=chunk[0]['xgrid']
dt=np.dtype([("ygrid","<51f")])
fd=open("/home/yu/Dropbox/workspace/data_assimilation/Optimal_interpolation/ygrid.out","r")
chunk=np.fromfile(fd,dtype=dt,count=1)
ygrid=chunk[0]['ygrid']
print xgrid
print ygrid
X,Y=np.meshgrid(xgrid,ygrid)
plt.title('Optinal interpolated SST',fontsize=25)
plt.set_cmap("jet")
cb_min=2
cb_max=14
interval_of_cf=subroutine.cl_res(cb_min,cb_max)
cmap=cm.jet
cf=pl.contourf(X,Y,data,interval_of_cf,cmap=cmap) # 色塗りコンター
interval=np.arange(0,100,2)
cr=pl.contour(X,Y,data,interval) # 普通のコンター
cr.clabel(fontsize=20,fmt='%d') # コンターラベルをつける
SC=plt.scatter(xcor,ycor,c=temp,s=75,vmin=min(interval_of_cf),vmax=max(interval_of_cf),cmap=cmap)
cb=pl.colorbar(cf) # カラーバーをつける(カラーコンター)
cb.set_label('Temperature') # カラーバーにタイトルを加える
pl.show()