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我使用 python 创建 netCDF 文件。当我尝试将值（数据）分配给变量的部分（或切片）时，取决于切片的“类型”是什么，我可以或不能分配这些值。

我不知道为什么。任何有助于理解为什么会这样的帮助将不胜感激。

例如： 

import numpy as np
from netCDF4 import Dataset

nb_steps = 2
nb_lat = 3
nb_lon = 4

# open/create file
f = Dataset('/home/ccorbel/Desktop/test.nc', 'w', format='NETCDF3_CLASSIC')
f.createDimension('lat', nb_lat)
f.createDimension('lon', nb_lon)
f.createDimension('time', nb_steps)

# create/fill variables
variables = {}
variables['t'] = f.createVariable('temperature', 'float64', ('time', 'lat', 'lon'))
variables['t'][...] = np.zeros((nb_steps, nb_lat, nb_lon))

# "equivalent" to [0, :, ::-1]
slc  = [0, slice(None, None, None), slice(None, None, -1)]    

# "equivalent" to [0, :, :]
slc2 = [0, slice(None, None, None), slice(None, None, None)] 

# "equivalent" to [:, ::-1]
slc3 = [   slice(None, None, None), slice(None, None, -1)]

print type(variables['t'])
# type 'netCDF4.Variable'
print type(variables['t'][slc])
# type 'numpy.ndarray'
print type(variables['t'][slc][...])
# type 'numpy.ndarray'
print np.shape(variables['t'][slc])
# (3, 4)

# variables['t'][slc] = np.random.random((nb_lat, nb_lon))
# return IndexError: too many indices

variables['t'][slc][...] = np.random.random((nb_lat, nb_lon))
print '\n', variables['t'][...]

# [[[ 0.  0.  0.  0.]
#   [ 0.  0.  0.  0.]
#   [ 0.  0.  0.  0.]]
# 
#  [[ 0.  0.  0.  0.]
#   [ 0.  0.  0.  0.]
#   [ 0.  0.  0.  0.]]]

variables['t'][...] = np.zeros((nb_steps, nb_lat, nb_lon)) # reset
variables['t'][slc2] = np.random.random((nb_lat, nb_lon))[slc3]
print '\n', variables['t'][...]

# [[[ 0.17502009  0.98414122  0.89686025  0.11072791]
#   [ 0.51351626  0.09234043  0.54314083  0.937711  ]
#   [ 0.98732418  0.22714407  0.87387761  0.44653219]]

#  [[ 0.          0.          0.          0.        ]
#   [ 0.          0.          0.          0.        ]
#   [ 0.          0.          0.          0.        ]]]

variables['t'][...] = np.zeros((nb_steps, nb_lat, nb_lon)) # reset
#variables['t'][0, :, ::-1] = np.random.random((nb_lat, nb_lon)) 
# return IndexError: too many indices

variables['t'][0, :, ::-1][...] = np.random.random((nb_lat, nb_lon))
print '\n', variables['t'][...]

# [[[ 0.  0.  0.  0.]
#   [ 0.  0.  0.  0.]
#   [ 0.  0.  0.  0.]]

#  [[ 0.  0.  0.  0.]
#   [ 0.  0.  0.  0.]
#   [ 0.  0.  0.  0.]]]

variables['t'][...] = np.zeros((nb_steps, nb_lat, nb_lon)) # reset
variables['t'][0, :, :] = np.random.random((nb_lat, nb_lon))[:, ::-1]
print '\n', variables['t'][...]

# [[[ 0.61406835  0.11069783  0.28667398  0.45018246]
#   [ 0.3833354   0.98871281  0.55559104  0.60415683]
#   [ 0.75200954  0.75106639  0.11688565  0.14264615]]

#  [[ 0.          0.          0.          0.        ]
#   [ 0.          0.          0.          0.        ]
#   [ 0.          0.          0.          0.        ]]]

variables['t'][...] = np.zeros((nb_steps, nb_lat, nb_lon)) # reset
variables['t'][0, :, :] = np.random.random((nb_lat, nb_lon))[slc3]
print '\n', variables['t'][...]

# [[[ 0.09437484  0.45757906  0.81116891  0.23580254]
#   [ 0.37316425  0.06768454  0.20259876  0.42127472]
#   [ 0.78879307  0.62535419  0.08942293  0.68789143]]

#  [[ 0.          0.          0.          0.        ]
#   [ 0.          0.          0.          0.        ]
#   [ 0.          0.          0.          0.        ]]]

f.close()
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1 回答 1

2

您的示例代码似乎可以在我的机器上运行，但我认为您可能遇到了问题，因为您在作业左侧使用了多个索引。A[0, :, ::-1][...] = something数组在哪里A很奇怪，即使它似乎在我的机器上工作，我也会尽量避免它。如果这不能解决您的问题，您能否给我们一个更清晰的示例来说明您所看到的问题（希望在左侧只有一个索引操作=），或者解释为什么要使用两个索引操作。

于 2011-12-19T21:37:48.713 回答