您可以創建並通過切片陣列操作與numpy的
# Generate a random 4d array that has nx = 3, ny = 3, and nz = 3, with each 3D point having 2 values
mdarray = np.random.random(size = (3,3,3,2))
# The overall shape of the 4d array
mdarray
Out[66]:
array([[[[ 0.80091246, 0.28476668],
[ 0.94264747, 0.27247111],
[ 0.64503087, 0.13722768]],
[[ 0.21371798, 0.41006764],
[ 0.79783723, 0.02537987],
[ 0.80658387, 0.43464532]],
[[ 0.04566927, 0.74836831],
[ 0.8280196 , 0.90288647],
[ 0.59271082, 0.65910184]]],
[[[ 0.82533798, 0.29075978],
[ 0.76496127, 0.1308289 ],
[ 0.22767752, 0.01865939]],
[[ 0.76849458, 0.7934015 ],
[ 0.93313128, 0.88436557],
[ 0.06897508, 0.00307739]],
[[ 0.15975812, 0.00792386],
[ 0.40292818, 0.21209199],
[ 0.48805502, 0.71974702]]],
[[[ 0.66522525, 0.49797465],
[ 0.29369336, 0.68743839],
[ 0.46411967, 0.69547356]],
[[ 0.50339875, 0.66423777],
[ 0.80520751, 0.88115054],
[ 0.08296022, 0.69467829]],
[[ 0.76572574, 0.45332754],
[ 0.87982243, 0.15773385],
[ 0.5762041 , 0.91268172]]]])
# Both values for this specific sample at x = 0, y = 1 and z = 2
mdarray[0,1,2]
Out[67]: array([ 0.80658387, 0.43464532])
mdarray[0,1,2,0] # mean only at the same point
Out[68]: 0.8065838666297338
mdarray[0,1,2,1] # variance only at the same point
Out[69]: 0.43464532443865489
您也可以單獨只獲取手段或分散值的多維數組:
mean = mdarray[:,:,:,0]
variance = mdarray[:,:,:,1]
mean
Out[74]:
array([[[ 0.80091246, 0.94264747, 0.64503087],
[ 0.21371798, 0.79783723, 0.80658387],
[ 0.04566927, 0.8280196 , 0.59271082]],
[[ 0.82533798, 0.76496127, 0.22767752],
[ 0.76849458, 0.93313128, 0.06897508],
[ 0.15975812, 0.40292818, 0.48805502]],
[[ 0.66522525, 0.29369336, 0.46411967],
[ 0.50339875, 0.80520751, 0.08296022],
[ 0.76572574, 0.87982243, 0.5762041 ]]])
我仍然不確定我如何傾向於繪製這些數據,會仔細考慮這一點並更新此答案。
你究竟在做什麼? Python沒有任何內置類型稱爲「矢量」?你的意思是一個「列表」?或者,你提到'numpy',你的意思是一些'numpy'數據結構? 'A [x,y,z]'意味着一個*三維*數組。 –