问题描述
如何使用 list_ 或 numpy.where() 将 numpy.select() 排列成数字组。我想将数据分成 3 组，然后计算这些函数的标准偏差 std()。因此，程序将采用 457.334015,424.440002,394.795990 作为要计算的第一个标准偏差值，并将采用 424.440002,394.795990,408.903992 并计算标准偏差等。它会一直这样下去，直到最后达到list 的结尾。我希望在计算第二个块 457.334015,394.795990 之前删除第一个块 424.440002,408.903992。我想从内存中删除块，所以我没有内存错误。这是否可以使用 numpy 并且不使用 for 循环。
number = 3
list_= np.array([457.334015,408.903992,398.821014,402.152008,435.790985,423.204987,411.574005,404.424988,399.519989,377.181000,375.467010,386.944000,383.614990,375.071991,359.511993,328.865997,320.510010,330.079010,336.187012,352.940002,365.026001,361.562012,362.299011,378.549011,390.414001,400.869995,394.773010,382.556000])
解决方法
来自this post：
from numpy.lib.stride_tricks import as_strided
def strided_app(a,L,S ): # Window len = L,Stride len/stepsize = S
nrows = ((a.size-L)//S)+1
n = a.strides[0]
return np.lib.stride_tricks.as_strided(a,shape=(nrows,L),strides=(S*n,n))
list_= np.array([457.334015,424.440002,394.795990,408.903992,398.821014,402.152008,435.790985,423.204987,411.574005,404.424988,399.519989,377.181000,375.467010,386.944000,383.614990,375.071991,359.511993,328.865997,320.510010,330.079010,336.187012,352.940002,365.026001,361.562012,362.299011,378.549011,390.414001,400.869995,394.773010,382.556000])
np.std(strided_app(list_,3,1),axis=1)
但是，此代码不会从数组中删除任何元素。另外，请记住，此处使用的函数带有来自 numpy 文档的 warning！