python - WRF netcdf 文件 - 基于 python 中的坐标边界从数据集中取出较小的数组

Question

我有两个来自 WRF 运行的 netcdf 文件，一个包含每小时数据，另一个较小的文件包含坐标(XLAT 和 XLONG)。我正在尝试根据某些坐标检索数据的子集。

其中一个变量的示例是温度“T2”，其维度 (1,1015,1359) 分别为(时间、south_north、west_east)。

XLAT 和 XLONG 具有相同的尺寸 (1,1015,1359)。

有人问了一个相同的问题(请参阅 netcdf4 extract for subset of lat lon )，因为我的纬度/经度尺寸有点不同，所以脚本对我不起作用，我无法弄清楚为什么。我尝试将坐标更改为一维数组，以便它与上一个问题类似，但脚本不起作用，并且出现索引错误。

如果有人可以帮助我，那就太棒了!预先感谢:)

import numpy as np
from netCDF4 import Dataset  
import matplotlib.pyplot as plt

lons = b.variables['XLONG'][:]
lats = b.variables['XLAT'][:]

lons2d =lons.reshape((1015,1359))
lons1d = lons2d.reshape((1379385))

lats2d =lats.reshape((1015,1359))
lats1d = lats2d.reshape((1379385))

lat_bnds, lon_bnds = [49,53], [-125,-115]
lat_inds = np.where((lats1d > lat_bnds[0]) & (lats1d < lat_bnds[1]))
lon_inds = np.where((lons1d > lon_bnds[0]) & (lons1d < lon_bnds[1]))

T_subset = a.variables['T2'][:,lat_inds,lon_inds]

但是我收到以下错误:

---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
<ipython-input-2-0f8890d3b1c5> in <module>()
 25 lon_inds = np.where((lons1d > lon_bnds[0]) & (lons1d < lon_bnds[1]))
 26 
---> 27 T_subset = a.variables['T2'][:,lat_inds,lon_inds]
 28 
 29 

netCDF4/_netCDF4.pyx in      netCDF4._netCDF4.Variable.__getitem__(netCDF4/_netCDF4.c:35672)()

/Users/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/netCDF4/utils.pyc in _StartCountStride(elem, shape, dimensions, grp, datashape, put)
197         # Raise error if multidimensional indexing is used.
198         if ea.ndim > 1:
--> 199             raise IndexError("Index cannot be multidimensional")
200         # set unlim to True if dimension is unlimited and put==True
201         # (called from __setitem__)
IndexError: Index cannot be multidimensional

Answer 1

我不确定为什么它不起作用，但我认为这符合您的要求并且更干净:

import numpy as np
from netCDF4 import Dataset
import matplotlib.pyplot as plt

# By indexing at 0 along first dimension, we eliminate the time
# dimension, which only had size 0 anyway.
lons = b.variables['XLONG'][0]
lats = b.variables['XLAT'][0]
temp = a.variables['T2'][0]

lat_bnds, lon_bnds = [49,53], [-125,-115]

# Just AND together all of them and make a big mask
subset = ((lats > lat_bnds[0]) & (lats < lat_bnds[1]) & 
          (lons > lon_bnds[0]) & (lons < lon_bnds[1]))

# Apply mask--should apply to trailing dimensions...I think
T_subset = temp[subset]