如何用python netCDF4创建一个netCDF文件？

Question

我正在学习如何在Anaconda中使用Pyhton模块使用netCDF4。我想追加值的两个变量，我创建time和field：

from netCDF4 import Dataset 
import numpy as np 

root_grp = Dataset('py_netcdf4.nc', 'w', format='NETCDF4') 
root_grp.description = 'Example simulation data' 

ndim = 128 # Size of the matrix ndim*ndim 
xdimension = 0.75 
ydimension = 0.75 
# dimensions 
root_grp.createDimension('time', None) 
root_grp.createDimension('x', ndim) 
root_grp.createDimension('y', ndim) 

# variables 
time = root_grp.createVariable('time', 'f8', ('time',)) 
x = root_grp.createVariable('x', 'f4', ('x',)) 
y = root_grp.createVariable('y', 'f4', ('y',)) 
field = root_grp.createVariable('field', 'f8', ('time', 'x', 'y',)) 

# data 
x_range = np.linspace(0, xdimension, ndim) 
y_range = np.linspace(0, ydimension, ndim) 
x[:] = x_range 
y[:] = y_range 
for i in range(5): 
    time[i] = i*50.0 
    field[i,:,:] = np.random.uniform(size=(len(x_range), len(y_range))) 


root_grp.close 

但现在当我打印的变量，我得到的一个，它是空的（!!）：

Python 2.7.10 |Anaconda 2.4.1 (64-bit)| (default, Sep 15 2015, 14:50:01) 
[GCC 4.4.7 20120313 (Red Hat 4.4.7-1)] on linux2 
Type "help", "copyright", "credits" or "license" for more information. 
Anaconda is brought to you by Continuum Analytics. 
Please check out: http://continuum.io/thanks and https://anaconda.org 
>>> from netCDF4 import Dataset 
>>> root_grp = Dataset('py_netcdf4.nc', 'r', format='NETCDF4') 
>>> print root_grp.variables["field"][:,:,:] 
[] 
>>> 

什么我在这里做错了吗？

Answer 1

这工作：

from netCDF4 import Dataset 
import numpy as np 

root_grp = Dataset('py_netcdf4.nc', 'w', format='NETCDF4') 
root_grp.description = 'Example simulation data' 

ndim = 128 # Size of the matrix ndim*ndim 
xdimension = 0.75 
ydimension = 0.75 
# dimensions 
root_grp.createDimension('time', None) 
root_grp.createDimension('x', ndim) 
root_grp.createDimension('y', ndim) 

# variables 
time = root_grp.createVariable('time', 'f8', ('time',)) 
x = root_grp.createVariable('x', 'f4', ('x',)) 
y = root_grp.createVariable('y', 'f4', ('y',)) 
field = root_grp.createVariable('field', 'f8', ('time', 'x', 'y',)) 

# data 
x_range = np.linspace(0, xdimension, ndim) 
y_range = np.linspace(0, ydimension, ndim) 
x[:] = x_range 
y[:] = y_range 
for i in range(5): 
    time[i] = i*50.0 
    field[i,:,:] = np.random.uniform(size=(len(x_range), len(y_range))) 


root_grp.close() 

唯一的区别是，我称之为close()方法：root_grp.close()。