我想保存一个气候文件，这样每次我需要计算异常时，我就不必再次运行气候脚本(这需要很多时间!)。 这些文件显然是以这种方式“CODEYYYYMMTTTT”:
hgap1981040000.nc
hgap1981040600.nc
hgap1981041200.nc
hgap1981041800.nc
我尝试使用下面的脚本将气候学平均值(从 netcdf 计算)保存到 netcdf 文件，但出现错误。
from pylab import *
import netCDF4 as nc
import numpy as np
u_2a=[]
v_2a=[]
w_2a=[]
u_2m=[]
v_2m=[]
w_2m=[]
#function to calculate mean values (this case, Apr-May only)
def mon(mo):
for yr in range (1981,1984,1):
dir_erai = '~/era-in/netc/monthly_means/{}/hgap{}{}????.nc'.format(yr,yr,mo)
print yr
f = nc.MFDataset(dir_erai)
uwnd = f.variables['U']
vwnd = f.variables['V']
wwnd = f.variables['W']
u_2 = np.mean(uwnd[0:4,:,:,:],axis=0)
v_2 = np.mean(vwnd[0:4,:,:,:],axis=0)
w_2 = np.mean(vwnd[0:4,:,:,:],axis=0)
f.close()
u_2a.append(u_2)
v_2a.append(v_2)
w_2a.append(w_2)
u_2m=np.mean(u_2a,axis=0)
v_2m=np.mean(v_2a,axis=0)
w_2m=np.mean(w_2a,axis=0)
return u_2m,v_2m,w_2m
uapr,vapr,wapr = mon('04')
umay,vmay,wmay = mon('05')
uAM = np.mean([uapr,umay],axis=0)
vAM = np.mean([vapr,vmay],axis=0)
wAM = np.mean([wapr,wmay],axis=0)
root_grp = Dataset('climatology_test.nc', 'w', format='NETCDF4')
root_grp.description = 'Example climatology winds UVW'
# dimensions
root_grp.createDimension('time', None)
root_grp.createDimension('lev', 37)
root_grp.createDimension('lat', 256)
root_grp.createDimension('lon', 512)
# variables
times = root_grp.createVariable('time', 'f8', ('time',))
levels = root_grp.createVariable('level', 'f4', ('lev',))
latitudes = root_grp.createVariable('latitude', 'f4', ('lat',))
longitudes = root_grp.createVariable('longitude', 'f4', ('lon',))
U1 = root_grp.createVariable('U1', 'f4', ('time', 'lev', 'lat', 'lon',))
V1 = root_grp.createVariable('V1', 'f4', ('time', 'lev', 'lat', 'lon',))
W1 = root_grp.createVariable('W1', 'f4', ('time', 'lev', 'lat', 'lon',))
# data
levs = [1000.,975.,950.,925.,900.,875.,850.,825.,800.,775.,750.,700.,650.,600.,550.,500.,450.,400.,350.,300.,250.,200.,175.,150.,125.,100.,70.,50.,30.,20.,10.,7.,5.,3.,2.,1.,0]
lats = np.arange(-89.5, 89.5, 0.70)
lons = np.arange(0., 358.4, 0.70)
levels[:] = levs
latitudes[:] = lats
longitudes[:] = lons
uAM[:,:,:,:] = np.random.uniform(size=(len(levs), len(lats), len(lons)))
vAM[:,:,:,:] = np.random.uniform(size=(len(levs), len(lats), len(lons)))
wAM[:,:,:,:] = np.random.uniform(size=(len(levs), len(lats), len(lons)))
root_grp.close()
和错误:
Traceback (most recent call last):
File "era_uv_climatology.py", line 99, in <module>
uAM[:,:,:,:] = np.random.uniform(size=(len(levs), len(lats), len(lons)))
IndexError: too many indices
我确实改变了
uAM[:,:,:,:] = np.random.uniform(size=(len(levs), len(lats), len(lons)))
vAM[:,:,:,:] = np.random.uniform(size=(len(levs), len(lats), len(lons)))
wAM[:,:,:,:] = np.random.uniform(size=(len(levs), len(lats), len(lons)))
到
U1 = uAM
V1 = vAM
W1 = wAM
我得到了一个错误的空 netCDF 文件，其中所有风值都为零，并且经纬度范围 (1,2,3,...,256) 和 (1,2,3... .,512) .
平均方法或赋值错误吗？还是两者兼而有之？
请您参考如下方法:
这第一段代码，
uAM[:,:,:,:] = np.random.uniform(size=(len(levs), len(lats), len(lons)))
vAM[:,:,:,:] = np.random.uniform(size=(len(levs), len(lats), len(lons)))
wAM[:,:,:,:] = np.random.uniform(size=(len(levs), len(lats), len(lons)))
给你这个错误是因为你的随机制服的形状与 netcdf 变量不一样。试试吧。
uAM[0,:,:,:] = np.random.uniform(size=(len(levs), len(lats), len(lons)))
vAM[0,:,:,:] = np.random.uniform(size=(len(levs), len(lats), len(lons)))
wAM[0,:,:,:] = np.random.uniform(size=(len(levs), len(lats), len(lons)))
你最后是用close方法关闭气候文件吗？
