切片類型影響numpy陣列的可變性

Question

我使用python創建netCDF文件。當我嘗試將值（數據）分配給變量的部分（或切片）時，取決於切片的「類型」，我可以或不能分配這些值。

我不知道爲什麼。任何有助於瞭解爲什麼這是將不勝感激。

例如爲：

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() 

Answer 1

你的示例代碼，似乎我的機器上工作，但我想你可能會因爲你使用你的任務左側的多個索引有問題。 A[0, :, ::-1][...] = something其中A是一個奇怪的數組，雖然它似乎在我的機器上工作，但我會盡量避免它。如果這不能解決您的問題，那麼您可以給我們一個更清晰的問題示例（希望在=的左側只有一個索引操作），或解釋爲什麼要使用兩個索引操作。