我正在尝试生成3D坐标列表，然后仅选择欧几里德范数间隔大于设定值的坐标。坐标列表可以长达10 ^ 6，我想有效地执行此操作（如果可能，使用numpy）。
编辑不包括尝试递归实施（这不起作用）
改编自http://www.arvindravichandran.com/articles/distance/
L = int(1e3) # simulation box dimension
dim = 3 # Dimensions
N = myran.randint(1, L**2, size=1)[0] # Number of coords
rangN = np.arange(N)
# Generate random positions
r = (np.random.randint(0, L - 1, size=(N, dim))) # coords
# uti is a list of two (1-D) numpy arrays
# containing the indices of the upper triangular matrix
uti = np.triu_indices(N, k=1) # k=1 eliminates diagonal indices
# uti[0] is i, and uti[1] is j from the previous example
dr = r[uti[0]] - r[uti[1]] # computes differences between positions
D = np.sqrt(np.einsum('...i,...i', dr, dr)) # computes distances; flat array
ind = D <= 1 # getting grouped indices
good = [x for x in rangN if x not in set((*uti[0][ind], *uti[1][ind]))] +\
[x for x in set(uti[0][ind]) if x not in uti[1][ind]]
locs = r[good]
警告。运行上面的代码块会使用很多的内存